Journal articles on the topic 'Twitter-based Surveillance'

Policymakers and relevant public health authorities can analyze people’s attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people’s attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people’s attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people’s attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.

Drug use reporting is often a bottleneck for modern public health surveillance; social media data provides a real-time signal which allows for tracking and monitoring opioid overdoses. In this work we focus on text-based feature construction for the prediction task of opioid overdose rates at the county level. More specifically, using a Twitter dataset with over 3.4 billion tweets, we explore semantic features, such as topic features, to show that social media could be a good indicator for forecasting opioid overdose crude rates in public health monitoring systems. Specifically, combining topic and TF-IDF features in conjunction with demographic features can predict opioid overdose rates at the county level.

“Web 2.0” platforms such as YouTube, MySpace, Facebook, Flickr, and Twitter that focus on data sharing, communication, community, and co-production have become very popular. It is therefore important to understand the economic organization of these platforms. The discussion of surveillance in web 2.0 is important because such platforms collect huge amounts of personal data in order to work. In this paper, first the example of Google Buzz is discussed. Then, a model that conceptualizes the cycle of capital accumulation and distinguishes between production and circulation of capital is introduced. The role of surveillance in web 2.0 is outlined based on the cycle of capital accumulation. The notions of the Internet prosumer commodity and web 2.0 surveillance are introduced in order to characterize the relationship of production, consumption, and surveillance on web 2.0.

Background Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. Objective This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. Methods This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Results Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). Conclusions To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

Purpose This study aims to determine the influence of social networking Twitter library on the level of utilization of college libraries. Design/methodology/approach The method used to explore this problem is explanative as it uses the multiple linear regression test. The research location is at Universitas Airlangga Library, Surabaya, Indonesia. The sampling technique was purposive, as it used the criteria of the academic community of Airlangga University that became an active member or often visited the homepage of the library’s Twitter account at least three times in the past one month. The sample of this study was 220 respondents. Findings The results of this study illustrate that the satisfaction of users of social networking Twitter library which includes surveillance, diversion/entertainment, personal identity and social relationship, either partially or simultaneously, affects the utilization of the college library. Originality/value Based on these results, libraries should always provide services in accordance with the characteristics of user behavior. In the era of booming social media Twitter for teenagers who are the largest users of college libraries, the use of social networking medium Twitter is considered effective in the libraries of universities.

Background Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. Objective This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. Methods We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. Results LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. Conclusions We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

Background Social networks such as Twitter offer the clinical research community a novel opportunity for engaging potential study participants based on user activity data. However, the availability of public social media data has led to new ethical challenges about respecting user privacy and the appropriateness of monitoring social media for clinical trial recruitment. Researchers have voiced the need for involving users’ perspectives in the development of ethical norms and regulations. Objective This study examined the attitudes and level of concern among Twitter users and nonusers about using Twitter for monitoring social media users and their conversations to recruit potential clinical trial participants. Methods We used two online methods for recruiting study participants: the open survey was (1) advertised on Twitter between May 23 and June 8, 2017, and (2) deployed on TurkPrime, a crowdsourcing data acquisition platform, between May 23 and June 8, 2017. Eligible participants were adults, 18 years of age or older, who lived in the United States. People with and without Twitter accounts were included in the study. Results While nearly half the respondents—on Twitter (94/603, 15.6%) and on TurkPrime (509/603, 84.4%)—indicated agreement that social media monitoring constitutes a form of eavesdropping that invades their privacy, over one-third disagreed and nearly 1 in 5 had no opinion. A chi-square test revealed a positive relationship between respondents’ general privacy concern and their average concern about Internet research (P<.005). We found associations between respondents’ Twitter literacy and their concerns about the ability for researchers to monitor their Twitter activity for clinical trial recruitment (P=.001) and whether they consider Twitter monitoring for clinical trial recruitment as eavesdropping (P<.001) and an invasion of privacy (P=.003). As Twitter literacy increased, so did people’s concerns about researchers monitoring Twitter activity. Our data support the previously suggested use of the nonexceptionalist methodology for assessing social media in research, insofar as social media-based recruitment does not need to be considered exceptional and, for most, it is considered preferable to traditional in-person interventions at physical clinics. The expressed attitudes were highly contextual, depending on factors such as the type of disease or health topic (eg, HIV/AIDS vs obesity vs smoking), the entity or person monitoring users on Twitter, and the monitored information. Conclusions The data and findings from this study contribute to the critical dialogue with the public about the use of social media in clinical research. The findings suggest that most users do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate surveillance or a violation of privacy. However, researchers should remain mindful that some participants might find social media monitoring problematic when connected with certain conditions or health topics. Further research should isolate factors that influence the level of concern among social media users across platforms and populations and inform the development of more clear and consistent guidelines.

Background: Emerging evidence suggests that exposure to secondhand and thirdhand aerosol from electronic cigarettes may have serious health risks including respiratory and cardiovascular diseases. Social media data can help identify common locations referenced in vaping-related discussions and offer clues about where individuals vape. These insights can strengthen current tobacco regulations and prioritize new policies to improve public health. This study identified commonly referenced locations in vaping-related discussions on Twitter in 2018. Methods: Vaping-related posts to Twitter were obtained from 1 January 2018 to 31 December 2018. Rule-based classifiers categorized each Twitter post into 11 location-related categories (social venues, living spaces, stores, modes of transportation, schools, workplaces, healthcare offices, eateries, correctional facilities, religious institutions, and miscellaneous) using a data dictionary of location-related keywords (n = 290,816). Results: The most prevalent category was social venues (17.9%), followed by living spaces (16.7%), stores (15.9%), modes of transportation (15.5%), schools (14.9%), and workplaces (11.9%). Other categories pertained to: healthcare offices (2.0%), eateries (1.2%), correctional facilities (0.7%), and religious institutions (0.4%). Conclusion: This study suggests that locations related to socialization venues may be priority areas for future surveillance and enforcement of smoke-free air policies. Similarly, development and enforcement of similar policies at workplaces, schools and multi-unit housing may curb exposure to secondhand and thirdhand aerosol among the public.

Large shifts in medical, recreational, and illicit cannabis consumption in the United States have implications for personalizing treatment and prevention programs to a wide variety of populations. As such, considerable research has investigated clinical presentations of cannabis users in clinical and population-based samples. Studies leveraging big data, social media, and social network analysis have emerged as a promising mechanism to generate timely insights that can inform treatment and prevention research. This study extends a novel method called stochastic block modeling to derive communities of cannabis consumers as part of a complex social network on Twitter. A set of examples illustrate how this method can ascertain candidate samples of medical, recreational, and illicit cannabis users. Implications for research planning, intervention design, and public health surveillance are discussed.

Summary Objectives: To summarize current research in the field of Public Health and Epidemiology Informatics. Methods: The complete 2016 literature concerning public health and epidemiology informatics has been searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to allow the editorial team an enlightened selection of the best papers. Results: Among the 829 references retrieved from PubMed and Web of Science, three were finally selected as best papers. The first one compares Google, Twitter, and Wikipedia as tools for Influenza surveillance. The second paper presents a Geographic Knowledge-Based Model for mapping suitable areas for Rift Valley fever transmission in Eastern Africa. The last paper evaluates the factors associated with the visit of Facebook pages devoted to Public Health Communication. Conclusions: Surveillance is still a productive topic in public health informatics but other very important topics in public health are appearing.

Summary Objectives: To summarize current research in the field of Public Health and Epidemiology Informatics. Methods: The complete 2016 literature concerning public health and epidemiology informatics has been searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to allow the editorial team an enlightened selection of the best papers. Results: Among the 829 references retrieved from PubMed and Web of Science, three were finally selected as best papers. The first one compares Google, Twitter, and Wikipedia as tools for Influenza surveillance. The second paper presents a Geographic Knowledge-Based Model for mapping suitable areas for Rift Valley fever transmission in Eastern Africa. The last paper evaluates the factors associated with the visit of Facebook pages devoted to Public Health Communication. Conclusions: Surveillance is still a productive topic in public health informatics but other very important topics in public health are appearing.

Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by one to three weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the twelve continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.

ObjectivesWe examined the use of data from social media for surveillance of physical activity prevalence in the USA.MethodsWe obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1 382 284 geotagged physical activity tweets from 481 146 users (55.7% men and 44.3% women) in more than 2900 counties. We applied machine learning and statistical modelling to demonstrate sex and regional variations in preferred exercises, and assessed the association between reports of physical activity on Twitter and population-level inactivity prevalence from the US Centers for Disease Control and Prevention.ResultsThe association between physical inactivity tweet patterns and physical activity prevalence varied by sex and region. Walking was the most popular physical activity for both men and women across all regions (15.94% (95% CI 15.85% to 16.02%) and 18.74% (95% CI 18.64% to 18.88%) of tweets, respectively). Men and women mentioned performing gym-based activities at approximately the same rates (4.68% (95% CI 4.63% to 4.72%) and 4.13% (95% CI 4.08% to 4.18%) of tweets, respectively). CrossFit was most popular among men (14.91% (95% CI 14.52% to 15.31%)) among gym-based tweets, whereas yoga was most popular among women (26.66% (95% CI 26.03% to 27.19%)). Men mentioned engaging in higher intensity activities than women. Overall, counties with higher physical activity tweets also had lower leisure-time physical inactivity prevalence for both sexes.ConclusionsThe regional-specific and sex-specific activity patterns captured on Twitter may allow public health officials to identify changes in health behaviours at small geographical scales and to design interventions best suited for specific populations.

Background The current COVID-19 pandemic is associated with extensive individual and societal challenges, including challenges to both physical and mental health. To date, the development of mental health problems such as depressive symptoms accompanying population-based federal distancing measures is largely unknown, and opportunities for rapid, effective, and valid monitoring are currently a relevant matter of investigation. Objective In this study, we aim to investigate, first, the temporal progression of depressive symptoms during the COVID-19 pandemic and, second, the consistency of the results from tweets and survey-based self-reports of depressive symptoms within the same time period. Methods Based on a cross-sectional population survey of 9011 German adolescents and adults (n=4659, 51.7% female; age groups from 15 to 50 years and older) and a sample of 88,900 tweets (n=74,587, 83.9% female; age groups from 10 to 50 years and older), we investigated five depressive symptoms (eg, depressed mood and energy loss) using items from the Patient Health Questionnaire (PHQ-8) before, during, and after relaxation of the first German social contact ban from January to July 2020. Results On average, feelings of worthlessness were the least frequently reported symptom (survey: n=1011, 13.9%; Twitter: n=5103, 5.7%) and fatigue or loss of energy was the most frequently reported depressive symptom (survey: n=4472, 51.6%; Twitter: n=31,005, 34.9%) among both the survey and Twitter respondents. Young adult women and people living in federal districts with high COVID-19 infection rates were at an increased risk for depressive symptoms. The comparison of the survey and Twitter data before and after the first contact ban showed that German adolescents and adults had a significant decrease in feelings of fatigue and energy loss over time. The temporal progression of depressive symptoms showed high correspondence between both data sources (ρ=0.76-0.93; P<.001), except for diminished interest and depressed mood, which showed a steady increase even after the relaxation of the contact ban among the Twitter respondents but not among the survey respondents. Conclusions Overall, the results indicate relatively small differences in depressive symptoms associated with social distancing measures during the COVID-19 pandemic and highlight the need to differentiate between positive (eg, energy level) and negative (eg, depressed mood) associations and variations over time. The results also underscore previous suggestions of Twitter data’s potential to help identify hot spots of declining and improving public mental health and thereby help provide early intervention measures, especially for young and middle-aged adults. Further efforts are needed to investigate the long-term consequences of recurring lockdown phases and to address the limitations of social media data such as Twitter data to establish real-time public mental surveillance approaches.

Background Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. Objective The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. Methods This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Results Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. Conclusions Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.

Abstract The Istituto Nazionale di Geofisica e Vulcanologia (INGV) is in charge of earthquake monitoring and surveillance in the Italian territory as a part of the civil protection system. Technological improvements in the last years were taken into account for developing new protocols and software to upgrade all the procedures in the monitoring centers. Real-time earthquake evaluation consists of phase picks, preliminary and automatic hypocenters, local magnitudes and ground-motion parameters. The real-time analysis system presently in use at INGV is the starting point for a new multitier compound system that relies on four main components: an automatic earthquake detection and location system based on Earthworm; a new seismological relational database for parametric data; a full set of new webservices application programming interface specifications to share information and provide data at the application level and; finally, a set of multiplatform interactive revision tools developed to analyze, store, use, and distribute the seismic parameters in real time. These last three components are being completely developed ex-novo at INGV in Rome. Such a system has been engineered to communicate with the International Federation of Digital Seismic Networks standard webservices as well as custom home-made INGV services. Through its custom embedded features Caravel will allow the INGV personnel on duty for seismic surveillance to evaluate and review all automatic estimations before they are communicated to the Italian civil protection agency and then published by external tools through e-mail, SMS, Twitter, and webpages.

Noise pollution is a growing global public health concern. Among other issues, it has been linked with sleep disturbance, hearing functionality, increased blood pressure and heart disease. Individuals are increasingly using social media to express complaints and concerns about problematic noise sources. This behavior—using social media to post noise-related concerns—might help us better identify troublesome noise pollution hotspots, thereby enabling us to take corrective action. The present work is a concept case study exploring the use of social media data as a means of identifying and monitoring noise annoyance across the United Arab Emirates (UAE). We explored an extract of Twitter data for the UAE, comprising over eight million messages (tweets) sent during 2015. We employed a search algorithm to identify tweets concerned with noise annoyance and, where possible, we also extracted the exact location via Global Positioning System (GPS) coordinates) associated with specific messages/complaints. The identified noise complaints were organized in a digital database and analyzed according to three criteria: first, the main types of the noise source (music, human factors, transport infrastructures); second, exterior or interior noise source and finally, date and time of the report, with the location of the Twitter user. This study supports the idea that lexicon-based analyses of large social media datasets may prove to be a useful adjunct or as a complement to existing noise pollution identification and surveillance strategies.

ABSTRACTDuring disasters, people share their thoughts and emotions on social media and also provide information about the event. Mining the social media messages and updates can be helpful in understanding the emotional state of people during such unforeseen events as they are real-time data. The objective of this review is to explore the feasibility of using social media data for mental health surveillance as well as the techniques used for determining mental health using social media data during disasters. PubMed, PsycINFO, and PsycARTICLES databases were searched from 2009 to November 2018 for primary research studies. After screening and analyzing the records, 18 studies were included in this review. Twitter was the widely researched social media platform for understanding the mental health of people during a disaster. Psychological surveillance was done by identifying the sentiments expressed by people or the emotions they displayed in their social media posts. Classification of sentiments and emotions were done using lexicon-based or machine learning methods. It is not possible to conclude that a particular technique is the best performing one, because the performance of any method depends upon factors such as the disaster size, the volume of data, disaster setting, and the disaster web environment.

Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the large volume of information requires the development of algorithms that handle natural language efficiently. In the bibliography, it is possible to find different techniques to carry out these infodemiology studies. However, as far as our knowledge, there are no comprehensive studies that compare the accuracy of these techniques. Consequently, we conducted an infodemiology-based study to extract positive or negative utterances related to infectious diseases so that future syndromic surveillance systems can be improved. The contribution of this paper is two-fold. On the one hand, we use Twitter to compile and label a balanced corpus of infectious diseases with 6164 utterances written in Spanish and collected from Central America. On the other hand, we compare two statistical-models: word-grams and char-grams. The experimentation involved the analysis of different gram sizes, different partitions of the corpus, and two machine-learning classifiers: Random-Forest and Sequential Minimal Optimization. The results reach a 90.80% of accuracy applying the char-grams model with five-char-gram sequences. As a final contribution, the compiled corpus is released.

By their nature, digital games facilitate surveillance. They allow for the compilation of statistics, internal states, and rules to be recorded, thus hiding many of the internal workings from the players and making the games much more complex. This digitization makes it much easier to collect player data and metrics, and then, as a process of function creep, to use this data in new and innovative ways, such as improving the user experience, or subtly shaping users' in-game desires and behaviours. Increasingly, these practices have moved from non-game spaces into social networking sites and spaces of play.The "gamification" movement is benefiting from the increasing sophistication of such metrics. Gamification combines the playful design and feedback mechanisms from games with users' social profiles (e.g. Facebook, twitter, and LinkedIn) in non-game applications explicitly geared to drive behavioural change (e.g. weight loss, workplace productivity, educational tools, and consumer loyalty). As critics point out, gamified applications rely on the points, leaderboards, and badges often seen in games, but are not games in themselves (Deterding 2010; Bogost 2011). Advocates of the gamification movement - including Al Gore in a recent Games for Change keynote - argue that this monitoring and feedback makes difficult tasks more playful and enjoyable (McGonigal 2011; Gore 2011). However, the marketing and political discourse of using games to change behaviour in positive ways is quite different from messy actualities rooted in advertising, consumption, and intrusive user monitoring. The current potentials to ‘gamify’ life have incited debate on whether the spread of these points based systems heralds playful utopias or dystopic surveillant societies run by corporations and advertisers. This paper highlights the rise of gamification and the implications for surveillance studies. In particular, it focuses on describing the increasingly intrusive monitoring practices are propagated under the banner of fun and play.

SummaryObjectives: Detecting hints to public health threats as early as possible is crucial to prevent harm from the population. However, many disease surveillance strategies rely upon data whose collection requires explicit reporting (data transmitted from hospitals, laboratories or physicians). Collecting reports takes time so that the reaction time grows. Moreover, context information on individual cases is often lost in the collection process. This paper describes a system that tries to address these limitations by processing social media for identifying information on public health threats. The primary objective is to study the usefulness of the approach for supporting the monitoring of a population's health status.Methods: The developed system works in three main steps: Data from Twitter, blogs, and forums as well as from TV and radio channels are continuously collected and filtered by means of keyword lists. Sentences of relevant texts are classified relevant or irrelevant using a binary classifier based on support vector machines. By means of statistical methods known from biosurveillance, the relevant sentences are further analyzed and signals are generated automatically when unexpected behavior is detected. From the generated signals a subset is selected for presentation to a user by matching with user queries or profiles. In a set of evaluation experiments, public health experts assessed the generated signals with respect to correctness and relevancy. In particular, it was assessed how many relevant and irrelevant signals are generated during a specific time period.Results: The experiments show that the system provides information on health events identified in social media. Signals are mainly generated from Twitter messages posted by news agencies. Personal tweets, i.e. tweets from persons observing some symptoms, only play a minor role for signal generation given a limited volume of relevant messages. Relevant signals referring to real world outbreaks were generated by the system and monitored by epidemiologists for example during the European football championship. But, the number of relevant signals among generated signals is still very small: The different experiments yielded a proportion between 5 and 20% of signals regarded as “relevant” by the users. Vaccination or education campaigns communicated via Twitter as well as use of medical terms in other contexts than for outbreak reporting led to the generation of irrelevant signals.Conclusions: The aggregation of information into signals results in a reduction of monitoring effort compared to other existing systems. Against expectations, only few messages are of personal nature, reporting on personal symptoms. Instead, media reports are distributed over social media channels. Despite the high percentage of irrele vant signals generated by the system, the users reported that the effort in monitoring aggregated information in form of signals is less demanding than monitoring huge social-media data streams manually. It remains for the future to develop strategies for reducing false alarms.

Background Between 2016 and 2017, the national mortality rate involving opioids continued its escalation; opioid deaths rose from 42,249 to 47,600, bringing the public health crisis to a new height. Considering that 69% of adults in the United States use online social media sites, a resource that builds a more complete understanding of prescription drug misuse and abuse could supplement traditional surveillance instruments. The Food and Drug Administration has identified 5 key risks and consequences of opioid drugs—misuse, abuse, addiction, overdose, and death. Identifying posts that discuss these key risks could lead to novel information that is not typically captured by traditional surveillance systems. Objective The goal of this study was to describe the trends of online posts (frequency over time) involving abuse, misuse, addiction, overdose, and death in the United States and to describe the types of websites that host these discussions. Internet posts that mentioned fentanyl, hydrocodone, oxycodone, or oxymorphone were examined. Methods Posts that did not refer to personal experiences were removed, after which 3.1 million posts remained. A stratified sample of 61,000 was selected. Unstructured data were classified into 5 key risks by manually coding for key outcomes of misuse, abuse, addiction, overdose, and death. Sampling probabilities of the coded posts were used to estimate the total post volume for each key risk. Results Addiction and misuse were the two most commonly discussed key risks for hydrocodone, oxycodone, and oxymorphone. For fentanyl, overdose and death were the most discussed key risks. Fentanyl had the highest estimated number of misuse-, overdose-, and death-related mentions (41,808, 42,659, and 94,169, respectively). Oxycodone had the highest estimated number of abuse- and addiction-related mentions (3548 and 12,679, respectively). The estimated volume of online posts for fentanyl increased by more than 10-fold in late 2017 and 2018. The odds of discussing fentanyl overdose (odds ratios [OR] 4.32, 95% CI 2.43-7.66) and death (OR 5.05, 95% CI 3.10-8.21) were higher for social media, while the odds of discussing fentanyl abuse (OR 0.10, 95% CI 0.04-0.22) and addiction (OR 0.24, 95% CI 0.15-0.38) were higher for blogs and forums. Conclusions Of the 5 FDA-defined key risks, fentanyl overdose and death has dominated discussion in recent years, while discussion of oxycodone, hydrocodone, and oxymorphone has decreased. As drug-related deaths continue to increase, an understanding of the motivations, circumstances, and consequences of drug abuse would assist in developing policy responses. Furthermore, content was notably different based on media origin, and studies that exclusively use either social media sites (such as Twitter) or blogs and forums could miss important content. This study sets out sustainable, ongoing methodology for surveilling internet postings regarding these drugs.

Abstract Objective Prescription medication (PM) misuse and abuse is a major health problem globally, and a number of recent studies have focused on exploring social media as a resource for monitoring nonmedical PM use. Our objectives are to present a methodological review of social media–based PM abuse or misuse monitoring studies, and to propose a potential generalizable, data-centric processing pipeline for the curation of data from this resource. Materials and Methods We identified studies involving social media, PMs, and misuse or abuse (inclusion criteria) from Medline, Embase, Scopus, Web of Science, and Google Scholar. We categorized studies based on multiple characteristics including but not limited to data size; social media source(s); medications studied; and primary objectives, methods, and findings. Results A total of 39 studies met our inclusion criteria, with 31 (∼79.5%) published since 2015. Twitter has been the most popular resource, with Reddit and Instagram gaining popularity recently. Early studies focused mostly on manual, qualitative analyses, with a growing trend toward the use of data-centric methods involving natural language processing and machine learning. Discussion There is a paucity of standardized, data-centric frameworks for curating social media data for task-specific analyses and near real-time surveillance of nonmedical PM use. Many existing studies do not quantify human agreements for manual annotation tasks or take into account the presence of noise in data. Conclusion The development of reproducible and standardized data-centric frameworks that build on the current state-of-the-art methods in data and text mining may enable effective utilization of social media data for understanding and monitoring nonmedical PM use.

In this work, we present a novel approach to understand the quality of public transit system in resource constrained regions using user-generated contents. With growing urban population, it is getting difficult to manage travel demand in an effective way. This problem is more prevalent in developing cities due to lack of budget and proper surveillance system. Due to resource constraints, developing cities have limited infrastructure to monitor transport services. To improve the quality and patronage of public transit system, authorities often use manual travel surveys. But manual surveys often suffer from quality issues. For example, respondents may not provide all the detailed travel information in a manual travel survey. The survey may have sampling bias. Due to close-ended design (specific questions in the questionnaire), lots of relevant information may not be captured in a manual survey process. To address these issues, we investigated if user-generated contents, for example, Twitter data, can be used to understand service quality in Greater Mumbai in India, which can complement existing manual survey process. To do this, we assumed that, if a tweet is relevant to public transport system and contains negative sentiment, then that tweet expresses user’s dissatisfaction towards the public transport service. Since most of the tweets do not have any explicit geolocation, we also presented a model that does not only extract users’ dissatisfaction towards public transit system but also retrieves the spatial context of dissatisfaction and the potential causes that affect the service quality. It is observed that a Random Forest-based model outperforms other machine learning models, while yielding 0.97 precision and 0.88 F1-score.

BackgroundThe Care Quality Commission (CQC) is responsible for ensuring the quality of healthcare in England. To that end, CQC has developed statistical surveillance tools that periodically aggregate large numbers of quantitative performance measures to identify risks to the quality of care and prioritise its limited inspection resource. These tools have, however, failed to successfully identify poor-quality providers. Facing continued budget cuts, CQC is now further reliant on an ‘intelligence-driven’, risk-based approach to prioritising inspections and a new effective tool is required.ObjectiveTo determine whether the near real-time, automated collection and aggregation of multiple sources of patient feedback can provide a collective judgement that effectively identifies risks to the quality of care, and hence can be used to help prioritise inspections.MethodsOur Patient Voice Tracking System combines patient feedback from NHS Choices, Patient Opinion, Facebook and Twitter to form a near real-time collective judgement score for acute hospitals and trusts on any given date. The predictive ability of the collective judgement score is evaluated through a logistic regression analysis of the relationship between the collective judgement score on the start date of 456 hospital and trust-level inspections, and the subsequent inspection outcomes.ResultsAggregating patient feedback increases the volume and diversity of patient-centred insights into the quality of care. There is a positive association between the resulting collective judgement score and subsequent inspection outcomes (OR for being rated ‘Inadequate’ compared with ‘Requires improvement’ 0.35 (95% CI 0.16 to 0.76), Requires improvement/Good OR 0.23 (95% CI 0.12 to 0.44), and Good/Outstanding OR 0.13 (95% CI 0.02 to 0.84), with p<0.05 for all).ConclusionsThe collective judgement score can successfully identify a high-risk group of organisations for inspection, is available in near real time and is available at a more granular level than the majority of existing data sets. The collective judgement score could therefore be used to help prioritise inspections.

Background Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade. Objective The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research. Methods The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment. Results Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%). Conclusions The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.

Background Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users’ demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. Objective We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. Methods This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users’ age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. Results The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. Conclusions We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users’ posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

Background College can be stressful for many freshmen as they cope with a variety of stressors. Excess stress can negatively affect both psychological and physical health. Thus, there is a need to find innovative and cost-effective strategies to help identify students experiencing high levels of stress to receive appropriate treatment. Social media use has been rapidly growing, and recent studies have reported that data from these technologies can be used for public health surveillance. Currently, no studies have examined whether Twitter data can be used to monitor stress level and emotional state among college students. Objective The primary objective of our study was to investigate whether students’ perceived levels of stress were associated with the sentiment and emotions of their tweets. The secondary objective was to explore whether students’ emotional state was associated with the sentiment and emotions of their tweets. Methods We recruited 181 first-year freshman students aged 18-20 years at University of California, Los Angeles. All participants were asked to complete a questionnaire that assessed their demographic characteristics, levels of stress, and emotional state for the last 7 days. All questionnaires were completed within a 48-hour period. All tweets posted by the participants from that week (November 2 to 8, 2015) were mined and manually categorized based on their sentiment (positive, negative, neutral) and emotion (anger, fear, love, happiness) expressed. Ordinal regressions were used to assess whether weekly levels of stress and emotional states were associated with the percentage of positive, neutral, negative, anger, fear, love, or happiness tweets. Results A total of 121 participants completed the survey and were included in our analysis. A total of 1879 tweets were analyzed. A higher level of weekly stress was significantly associated with a greater percentage of negative sentiment tweets (beta=1.7, SE 0.7; P=.02) and tweets containing emotions of fear (beta=2.4, SE 0.9; P=.01) and love (beta=3.6, SE 1.4; P=.01). A greater level of anger was negatively associated with the percentage of positive sentiment (beta=–1.6, SE 0.8; P=.05) and tweets related to the emotions of happiness (beta=–2.2, SE 0.9; P=.02). A greater level of fear was positively associated with the percentage of negative sentiment (beta=1.67, SE 0.7; P=.01), particularly a greater proportion of tweets related to the emotion of fear (beta=2.4, SE 0.8; P=.01). Participants who reported a greater level of love showed a smaller percentage of negative sentiment tweets (beta=–1.3, SE 0.7; P=0.05). Emotions of happiness were positively associated with the percentage of tweets related to the emotion of happiness (beta=–1.8, SE 0.8; P=.02) and negatively associated with percentage of negative sentiment tweets (beta=–1.7, SE 0.7; P=.02) and tweets related to the emotion of fear (beta=–2.8, SE 0.8; P=.01). Conclusions Sentiment and emotions expressed in the tweets have the potential to provide real-time monitoring of stress level and emotional well-being in college students.

Background:Systemic lupus erythematosus (SLE) is an auto-immune disease that affect women in their reproductive age. Antiphospholipid syndrome (APS) is a hypercoagulable immune disease that occur as a primary condition or in assosiation with SLE.The reproductive aspects as contraception, fertility, pregnancy are crucial to consider for proper management of SLE/APS.Addressing these issues require collaboration between rheumatologists and obstetricians, improving their knowledge and ensuring that both are acquainted with the updated guidelines.Objectives:To assess the knowledge and practice of Egyptian obstetricians and rheumatologists in management of reproductive health issues in SLE and APS, and to detect common misconceptions.Methods:This research was conducted via google form online survey based on points discussed in EULAR recommendations for women’s health and the management of family planning, assisted reproduction, pregnancy and menopause in patients with SLE and/or APS.1It was sent to target obstetricians and rheumatologists by internet clouds like (Facebook, twitter, LinkedIn) from August to November 2019. It included five domains; demographic data, general knowledge and attitudes about pregnancy in SLE and APS, contraception, drugs, and assisted reproductive techniques (ART)After submitting answers, respondents were shown a link directing them to the 2016 EULAR recommendations.1Results:This study was conducted on 254 physicians, 62% obstetricians and 38% rheumatologists. 64.6% were between the ages of 30-35 years.For general knowledge, 52% of Obstetricians considered pregnancy in inactive SLE to be risky. (79.4%vs54.1%) of (rheumatologists and obstetricians) respectively test for aPL in SLE patients. More than 70% in both groups were well informed on the increased rate of fetal and maternal complications in both SLE and APS.For fetal surveillance, 87% and 90% of obstetricians preformed first and second trimester U/S, and 79% preformed second trimester Doppler.For contraception, (57.7%vs56.7%) discuss contraceptive choices with their patients. The majority considered it safe to use IUDs (73.9%vs76%) and condoms (84.7vs85.4%) in both SLE and APS patients. On the other hand, for hormonal contraception- Levonorgestrol IUD, Depoprovera, COCP, and POP- only 14.6%, 22.9%, 26.1%, 24.8% of rheumatologists and 18.5%, 27.2%, 29.9%, 26.8% of obstetricians considered them unsafe to use in APS.Concerning treatment, the majority considered low dose presnidone to be safe during pregnancy (94.8 %vs80%) and breastfeeding (87.6%vs64.3%). The majority also agreed on avoidance of Methotrexate (94.8%vs84.1%) and Cyclophosphamide (89.7%vs66.2%). However, regarding Hydroxychloroquine and Azathioprine use in pregnancy there was a significant discrepancy between rheumatologists and obstetricians, (89.7 %vs42%) and (78.4%vs36.9%) believed them safe to use in pregnancy. For Mycophenolate Mofetil, (80.4%vs46.5%) said that it should be avoided in pregnancy. Regarding ART (45.4%vs71%) considered it safe to use in stable SLE/APS.Conclusion:The gaps in knowledge identified include the use of hormonal contraception in APS patients and the proper utilization of important medications to prevent and treat lupus flares. Initiation of shared Rheumatology/ obstetric clinics and focusing on the identified educational topics, would lessen the gap in knowledge and discrepancies in practice improve overall patient management.References:[1]Andreoli L et al. Ann Rheum Dis. 2017;76(3):476-85.Disclosure of Interests:None declared

Alvin Toffler once wrote: "The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn." This pandemic has proven his statement correct. The global academic community has learned a completely new culture of research, with torrents of data being released daily on preprint servers1,2 and dissected on platforms such as Slack and Twitter before formally peer reviewed. Fifty-five thousand viral genomes sequences of hCoV-19 shared on GISAID platforms to date3 that have been analyzed instantaneously, by a phalanx of evolutionary biologists who share their phylogenetic trees in preprints. Such advances have allowed scientists to trace and monitor the COVID-19 pandemic faster than any previous outbreak. There is still more to learn. The scientist from the fields of epidemiology, virology and biomedical science are struggling to keep this outbreak under control. Estimation of R0, which have been an important part of characterizing pandemics, including the 2003 SARS pandemic, the 2009 H1N1 influenza pandemic and the 2014 Ebola epidemic in West Africa, is something epidemiologists raced to nail down about SARS-CoV-2. There's uncertainty,foranumberofreasons,aboutmanyofthefactorsthatgointoestimatingR0. First,theincubation period of this viral pathogen is uncertain with an average 5-6 days and can be up to 14 days.4 Researchers cannot predict, without sentinel surveillance, the number of mild or asymptomatic cases that have been missed but nevertheless are spreading the disease.5 Secondly, majority of people who get infected, do recover and are likely to be immune. This alters population susceptibility and affects future trajectory of infection spread. Finally, susceptibility to disease in different communities varies based on their demographics, health conditions and different social structures. And hence, mathematical model accuracy, be it Institute for Health Metrics and Evaluation (IHME)6, Ferguson et.al7, Aleta et.al8, Hellewell et.al9 and Kessler et al models10, is constrained by our knowledge of the virus dynamics since many biologic features of transmission are hard to measure and remain unknown. Another aspect of the Covid-19, which is reshaping the world of bioscience publishing, is the tension between rapid speed of research publication verses scientific rigor. This has raised serious issues regarding data integrity. The Lancet and NEMJ had had to retract some publication on this account for example, Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis11 and Cardiovascular Disease, Drug Therapy, and Mortality in Covid-1912, because independent auditors were unable to validate the primary data sources. This is of concern in the middle of a global health emergency.13 Finally, this crisis has also altered our perspective. An important feature of our ongoing experience is what anthropologist Jane Guyer termed “enforced presentism”, a feeling of being stuck in the present, combined with an inability to plan ahead.14 The question is how do we reclaim our future? The past has provided us a prologue for discussion, whether it is the biological origins of a potential pandemic or its social repercussions, it is up to us to reorder the society in dramatic ways, for better or worse. Editor-in-Chief doi: http://doi.org/10.37185/LnS.1.1.127

Background: Influenza (flu) surveillance using Twitter data can potentially save lives and increase efficiency by providing governments and healthcare organizations with greater situational awareness. However, research is needed to determine the impact of Twitter users’ misdiagnoses on surveillance accuracy. Objective: This study establishes the importance of Twitter users’ misdiagnoses by showing that Twitter flu surveillance in the United States failed during the 2011-2012 flu season, estimates the extent of misdiagnoses, and tests several methods for reducing the adverse effects of misdiagnoses.Methods: Metrics representing flu prevalence, seasonal misdiagnosis patterns, diagnosis uncertainty, flu symptoms, and noise were produced using Twitter data in conjunction with OpenSextant for geo-inferencing, and a maximum entropy classifier for identifying tweets related to illness. These metrics were tested for correlations with World Health Organization (WHO) positive specimen counts of flu from 2011 to 2014.Results: Twitter flu surveillance erroneously indicated a typical flu season during 2011-2012, even though the flu season peaked three months late, and erroneously indicated plateaus of flu tweets before the 2012-2013 and 2013-2014 flu seasons. Enhancements based on estimates of misdiagnoses removed the erroneous plateaus and increased the Pearson correlation coefficients by .04 and .23, but failed to correct the 2011-2012 flu season estimate. A rough estimate indicates that approximately 40% of flu tweets reflected misdiagnoses.Conclusions: Further research into factors affecting Twitter users’ misdiagnoses, in conjunction with data from additional atypical flu seasons, is needed to enable Twitter flu surveillance systems to produce reliable estimates during atypical flu seasons.

AbstractSocial media services such as Twitter are valuable sources of information for surveillance systems. A digital syndromic surveillance system has several advantages including its ability to overcome the problem of time delay in traditional surveillance systems. Despite the progress made with using digital syndromic surveillance systems, the possibility of tracking avian influenza (AI) using online sources has not been fully explored. In this study, a Twitter-based data analysis framework was developed to automatically monitor avian influenza outbreaks in a real-time manner. The framework was implemented to find worrisome posts and alerting news on Twitter, filter irrelevant ones, and detect the onset of outbreaks in several countries. The system collected and analyzed over 209,000 posts discussing avian influenza on Twitter from July 2017 to November 2018. We examined the potential of Twitter data to represent the date, severity and virus type of official reports. Furthermore, we investigated whether filtering irrelevant tweets can positively impact the performance of the system. The proposed approach was empirically evaluated using a real-world outbreak-reporting source. We found that 75% of real-world outbreak notifications of AI were identifiable from Twitter. This shows the capability of the system to serve as a complementary approach to official AI reporting methods. Moreover, we observed that one-third of outbreak notifications were reported on Twitter earlier than official reports. This feature could augment traditional surveillance systems and provide a possibility of early detection of outbreaks. This study could potentially provide a first stepping stone for building digital disease outbreak warning systems to assist epidemiologists and animal health professionals in making relevant decisions.

ObjectiveTo comparatively analyze Google, Twitter, and Wikipedia byevaluating how well change points detected in each web-based sourcecorrespond to change points detected in CDC ILI data.IntroductionTraditional influenza surveillance relies on reports of influenza-like illness (ILI) by healthcare providers, capturing individualswho seek medical care and missing those who may search, post,and tweet about their illnesses instead. Existing research has shownsome promise of using data from Google, Twitter, and Wikipediafor influenza surveillance, but with conflicting findings, studies haveonly evaluated these web-based sources individually or dually withoutcomparing all three of them1-5. A comparative analysis of all threeweb-based sources is needed to know which of the web-based sourcesperforms best in order to be considered to complement traditionalmethods.MethodsWe collected publicly available, de-identified data from the CDCILINet system, Google Flu Trends, HealthTweets.org, and Wikipediafor the 2012-2015 influenza seasons. Bayesian change point analysiswas the method used to detect change points, or seasonal changes,in each of the web-data sources for comparison to change pointsin CDC ILI data. All analyses was conducted using the R package‘bcp’ v4.0.0 in RStudio v0.99.484. Sensitivity and positive predictivevalues (PPV) were then calculated.ResultsDuring the 2012-2015 influenza seasons, a high sensitivity of 92%was found for Google, while the PPV for Google was 85%. A lowsensitivity of 50% was found for Twitter; a low PPV of 43% wasfound for Twitter also. Wikipedia had the lowest sensitivity of 33%and lowest PPV of 40%.ConclusionsGoogle had the best combination of sensitivity and PPV indetecting change points that corresponded with change points found inCDC data. Overall, change points in Google, Twitter, and Wikipediadata occasionally aligned well with change points captured in CDCILI data, yet these sources did not detect all changes in CDC data,which could indicate limitations of the web-based data or signify thatthe Bayesian method is not adequately sensitive. These three web-based sources need to be further studied and compared using otherstatistical methods before being incorporated as surveillance data tocomplement traditional systems.Figure 1. Detection of change points, 2012-2013 influenza seasonFigure 2. Detection of change points, 2013-2014 influenza seasonFigure 3. Detection of change points, 2014-2015 influenza season

Abstract Background The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data collection as cost-effective and unobtrusive alternative for public health monitoring to complement the traditional survey-based approaches. Methods The data was extracted from Twitter based on pre-defined keywords (i.e. ’hayfever’ OR ’hay fever’) throughout the period of 6 months, covering the high pollen season in Australia. The following deep learning architectures were adopted in the experiments: CNN, RNN, LSTM and GRU. Both default (GloVe) and domain-specific (HF) word embeddings were used in training the classifiers. Standard evaluation metrics (i.e. Accuracy, Precision and Recall) were calculated for the results validation. Finally, visual correlation with weather variables was performed. Results The neural networks-based approach was able to correctly identify the implicit mentions of the symptoms and treatments, even unseen previously (accuracy up to 87.9% for GRU with GloVe embeddings of 300 dimensions). Conclusions The system addresses the shortcomings of the conventional machine learning techniques with manual feature-engineering that prove limiting when exposed to a wide range of non-standard expressions relating to medical concepts. The case-study presented demonstrates an application of ’black-box’ approach to the real-world problem, along with its internal workings demonstration towards more transparent, interpretable and reproducible decision-making in health informatics domain.

ObjectiveThe objective of this analysis is to leverage recent advances innatural language processing (NLP) to develop new methods andsystem capabilities for processing social media (Twitter messages)for situational awareness (SA), syndromic surveillance (SS), andevent-based surveillance (EBS). Specifically, we evaluated the useof human-in-the-loop semantic analysis to assist public health (PH)SA stakeholders in SS and EBS using massive amounts of publiclyavailable social media data.IntroductionSocial media messages are often short, informal, and ungrammatical.They frequently involve text, images, audio, or video, which makesthe identification of useful information difficult. This complexityreduces the efficacy of standard information extraction techniques1.However, recent advances in NLP, especially methods tailoredto social media2, have shown promise in improving real-time PHsurveillance and emergency response3. Surveillance data derived fromsemantic analysis combined with traditional surveillance processeshas potential to improve event detection and characterization. TheCDC Office of Public Health Preparedness and Response (OPHPR),Division of Emergency Operations (DEO) and the Georgia TechResearch Institute have collaborated on the advancement of PH SAthrough development of new approaches in using semantic analysisfor social media.MethodsTo understand how computational methods may benefit SS andEBS, we studied an iterative refinement process, in which the datauser actively cultivated text-based topics (“semantic culling”) in asemi-automated SS process. This ‘human-in-the-loop’ process wascritical for creating accurate and efficient extraction functions in large,dynamic volumes of data. The general process involved identifyinga set of expert-supplied keywords, which were used to collect aninitial set of social media messages. For purposes of this analysisresearchers applied topic modeling to categorize related messages intoclusters. Topic modeling uses statistical techniques to semanticallycluster and automatically determine salient aggregations. A user thensemantically culled messages according to their PH relevance.In June 2016, researchers collected 7,489 worldwide English-language Twitter messages (tweets) and compared three samplingmethods: a baseline random sample (C1, n=2700), a keyword-basedsample (C2, n=2689), and one gathered after semantically cullingC2 topics of irrelevant messages (C3, n=2100). Researchers utilizeda software tool, Luminoso Compass4, to sample and perform topicmodeling using its real-time modeling and Twitter integrationfeatures. For C2 and C3, researchers sampled tweets that theLuminoso service matched to both clinical and layman definitions ofRash, Gastro-Intestinal syndromes5, and Zika-like symptoms. Laymanterms were derived from clinical definitions from plain languagemedical thesauri. ANOVA statistics were calculated using SPSSsoftware, version. Post-hoc pairwise comparisons were completedusing ANOVA Turkey’s honest significant difference (HSD) test.ResultsAn ANOVA was conducted, finding the following mean relevancevalues: 3% (+/- 0.01%), 24% (+/- 6.6%) and 27% (+/- 9.4%)respectively for C1, C2, and C3. Post-hoc pairwise comparison testsshowed the percentages of discovered messages related to the eventtweets using C2 and C3 methods were significantly higher than forthe C1 method (random sampling) (p<0.05). This indicates that thehuman-in-the-loop approach provides benefits in filtering socialmedia data for SS and ESB; notably, this increase is on the basis ofa single iteration of semantic culling; subsequent iterations could beexpected to increase the benefits.ConclusionsThis work demonstrates the benefits of incorporating non-traditional data sources into SS and EBS. It was shown that an NLP-based extraction method in combination with human-in-the-loopsemantic analysis may enhance the potential value of social media(Twitter) for SS and EBS. It also supports the claim that advancedanalytical tools for processing non-traditional SA, SS, and EBSsources, including social media, have the potential to enhance diseasedetection, risk assessment, and decision support, by reducing the timeit takes to identify public health events.

Abstract Background The fight against the COVID-19 pandemic seems to encompass a social media debate, possibly resulting in emotional contagion and the need for novel surveillance approaches. In the current study, we aimed to examine the flow and content of tweets, exploring the role of COVID-19 key events on the popular Twitter platform. Methods Using representative freely available data, we performed a focused, social media-based analysis to capture COVID-19 discussions on Twitter, considering sentiment and longitudinal trends between January 19 and March 3, 2020. Different populations of users were considered. Core discussions were explored measuring tweets’ sentiment, by both computing a polarity compound score with 95% Confidence Interval and using a transformer-based model, pretrained on a large corpus of COVID-19-related Tweets. Context-dependent meaning and emotion-specific features were considered. Results We gathered 3,308,476 tweets written in English. Since the first World Health Organization report (January 21), negative sentiment proportion of tweets gradually increased as expected, with amplifications following key events. Sentiment scores were increasingly negative among most active users. Tweets content and flow revealed an ongoing scenario in which the global emergency seems difficult to be emotionally managed, as shown by sentiment trajectories. Conclusions Integrating social media like Twitter as essential surveillance tools in the management of the pandemic and its waves might actually represent a novel preventive approach to hinder emotional contagion, disseminating reliable information and nurturing trust. There is the need to monitor and sustain healthy behaviors as well as community supports also via social media-based preventive interventions.

ObjectiveTo investigate whether Twitter data can be used as a proxy for the surveillance of the seasonal influenza epidemic in France and at the regional level.IntroductionSocial media as Twitter are used today by people to disseminate health information but also to share or exchange on their health. Based on this observation, recent studies showed that Twitter data can be used to monitor trends of infectious diseases such as influenza. These studies were mainly carried out in United States where Twitter is very popular1-4. In our knowledge, no research has been implemented in France to know whether Twitter data can be a complementary data source to monitor seasonal influenza epidemic.MethodsFor this exploratory study, an R program allowing to the collection, pre-processing (geolocation and classification) and analysis of Tweets related to influenza-like illness was developed.CollectionStream API was used to collect Tweets in French language that contained terms “grippe”,”grippal”, “grippaux” without to specify geolocation coordinates.Pre-processIn order to identify Tweets localized in France, a combination of automated filters has been implemented. At the end, were retained:● Tweets with geolocation coordinates in France (GPS coordinates, country code, country, place name)● Tweets whose place indicated in user’s profile matched with a city, department or region of France● Tweets included FR-related time zone but excluding all Tweets reporting a FR time zone but a non-FR place-code.In the second time, a support vector machine (SVM) classifier was used to filter out noise from the database. To train the classifier, 1500 Tweets were randomly sampled. Each of these 1500 training Tweets was manually inspected and tagged as valid or invalid according to the likelihood that they indicated influenza-like illness. This hand-tagged training set was converted to vector representation using their term-frequency-inverse document frequency (TF-IDF) scores. These TF-IDF vectors were then input to the SVM for training. To evaluate performances of the classifier: accurency, recall and F- measure were calculated from a 1000 randomly sampled Tweets manually tagged.AnalysisData collected over the period from August 8, 2016 to March 26, 2017 were compared to those of the French syndromic surveillance system SurSaUD® (OSCOUR® and SOS Médecins network)5 by Spearman's rank correlation coefficient.EthicalIn accordance to the National Commission on Informatics and Liberty, information about user account were removed in database except location variables. Usernames contained in the text of the tweet have also been deleted.ResultsOver the study period, the system collected 238,244 influenza-related Tweets of which 130,559 were located in France. After a cleaning step, 22,939 Tweets were classified by the algorithm as an influenza-like illness (ILI). The performances of the classifier were 0.739 for accuracy, 0.725 for recall and 0.732 for F-measure. Figure 1 shows that the weekly number of ILI Tweets follows the same trend as the weekly number of ED visits and physicians consultations for ILI. Regardless of data source, Spearman's correlation coefficients were positive and statistically significant at the national level and for each region of France (Table 1).ConclusionsThis exploratory study allowed to show that Twitter data can be used to monitor the epidemic of seasonal influenza in France and at regional level, in complementarity with existing systems. The system needs to be improved to confirm the trends observed during the next influenza epidemic.References1.Broniatowski DA, Paul MJ, Dredze M. National and local influenza surveillance through Twitter: An analysis of the 2012-2013 influenza epidemic. PLoS One. 2013;8(12):e83672.2.Gesualdo F, Stilo G, Agricola E, Gonfiantini MV, Pandolfi E, Velardi P, et al. Influenza-like illness surveillance on Twitter through automated learning of naïve language. PLoS One. 2013;8(12):e82489.3. Paul MJ, Dredze M, Broniatowski D. Twitter improves influenza forecasting. PLoS Curr. 2014;6.4. Allen C, Tsou MH, Aslam A, Nagel A, Gawron JM. Applying GIS and machine learning methods to Twitter data for multiscale surveillance of influenza. PLoS One. 2016;11(7):e0157734.5. Ruello M, Pelat C, Caserio-Schönemann C, et al. A regional approach for the influenza surveillance in France. OJPHI. 2017;9(1):e089.

Research in Smart City development has been proliferated over the past few years, which focused heavily on various supporting service sectors, such as healthcare. However, little effort has been made to design health surveillance support systems, which is also important for the advancement of public healthcare monitoring as an essential smart city initiatives. From an information system (IS) design perspective, this paper introduces a social media-based health surveillance supporting method, which can automatically extricates relevant online posts for health symptom management and prediction. We describe and demonstrate an IS design approach in this paper for hay-fever prediction solution concept based on Twitter posts. This concept can be applicable to fully functional solution design by relevant practitioners in this field.

AbstractThe novel settings provided by social media facilitate users to seek and share information on a wide array of subjects, including healthcare and wellness. Analyzing health-related opinions and discussions on these platforms complement traditional public health surveillance systems to support timely and effective interventions. This study aims to characterize the HIV-related conversations on Twitter by identifying the prevalent topics and the key events and actors involved in these discussions. Through Twitter API, we collected tweets containing the hashtag #HIV for a one-year period. After pre-processing the collected data, we conducted engagement analysis, temporal analysis, and topic modeling algorithm on the analytical sample (n = 122,807). Tweets by HIV/AIDS/LGBTQ activists and physicians received the highest level of engagement. An upsurge in tweet volume and engagement was observed during global and local events such as World Aids Day and HIV/AIDS awareness and testing days for trans-genders, blacks, women, and the aged population. Eight topics were identified that include “stigma”, “prevention”, “epidemic in the developing countries”, “World Aids Day”, “treatment”, “events”, “PrEP”, and “testing”. Social media discussions offer a nuanced understanding of public opinions, beliefs, and sentiments about numerous health-related issues. The current study reports various dimensions of HIV-related posts on Twitter. Based on the findings, public health agencies and pertinent entities need to proactively use Twitter and other social media by engaging the public through involving influencers. The undertaken methodological choices may be applied to further assess HIV discourse on other popular social media platforms.

With developing technology today, social media has entered every area of our lives. Many people come together and share in social media platforms without time and space restrictions. Social media has been in our lives so much lately. It is an undeniable fact that global outbreaks, which constitute an important part of our lives, are also affected by these networks and that they exist in these networks and share the users. The purpose of making this hashtag analysis is to reveal the difference in discourse and language while analyzing twitter data, while doing this, to evaluate the effects of a global epidemic crisis on language, message and crisis management with social media data. Sentiment analysis of tweets, on the other hand, objectives to take a look at the contents of these messages, to degree the feelings and feelings conveyed. This form of analysis is typically completed through amassing textual content data, then investigating the “sentiment” conveyed. Within the scope of our study, one hundred thousand twitter messages posted with the #stayhome hashtag between 23 May 2020 and 29 May 2020 were examined. The impact and reliability of social media in disaster management could be questioned by carrying out a content analysis based totally on the semantic analysis of the messages given on the Twitter posts with the phrases and frequencies used. Social media and Twitter content are increasingly more identified as treasured resources of public health signals concerning use in ailment surveillance and health disaster management.

ObjectiveTo determine if social media data can be used as a surveillance tool for influenza at the local level.IntroductionThe use of social media as a syndromic sentinel for diseases is an emerging field of growing relevance as the public begins to share more online, particularly in the area of influenza. Several applications have been developed to predict or monitor influenza activity using publicly posted or self-reported online data; however, few have prioritized accuracy at the local level. In 2016, the Cook County Department of Public Health (CCDPH) collected localized Twitter information to evaluate its utility as a potential influenza sentinel data source. Tweets from MMWR week 40 through MMWR week 20 indicating influenza-like illness (ILI) in our jurisdiction were collected and analyzed for correlation with traditional surveillance indicators. Social media has the potential to join other sentinels, such as emergency room and outpatient provider data, to create a more accurate and complete picture of influenza in Cook County.MethodsWe developed a JAVA program which included a customized geofence around suburban Cook County to collect tweets from Twitter’s STREAM application programming interface (API) (available at https://github.com/FoodSafeCookCo/TwitterStream-Program). The JAVA program looked for tweets within the geofence or for tweets with a profile location naming a suburban Cook County municipality and copied them to a text file if the tweet contained “flu” or “influenza”. Captured data included the user’s Twitter handle, Tweet text, Tweet time and date, x and y coordinates (if available), and profile location. Tweets were then manually reviewed to determine if they met the following criteria: 1) language indicated the user was recently ill with influenza; 2) user appeared to reside in CCDPH jurisdiction. Tweets meeting these criteria were included in the analysis. Tweets were aggregated by MMWR week and analyzed for correlation, using Pearson methods (data were normal), with two traditional surveillance sources: 1) the percent of visits to all suburban Cook County emergency departments for ILI as reported to the Cook County Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), and 2) the percent of laboratory specimens testing positive for influenza at seven local sentinel laboratories. Analysis was performed in Excel 2013 and SAS 9.4.ResultsFrom MMWR week 40-20, 113 tweets indicating influenza-like illness were collected within Cook County’s jurisdiction. Due to technical issues with the program, data were not collected for weeks 52, 2, and 17-19. Correlations were compared for the percent of laboratory specimens testing positive for influenza (LSL) and percent of visits to emergency departments for ILI (EDILI) to the total number of tweets per MMWR week. A strong correlation exists between LSL and EDILI r=0.92 (p-value<0.0001) indicating the traditional sentinels have a strong positive relationship. The correlation between number of tweets and LSL was 0.46 (p-value =0.0138), indicating a moderate positive relationship. Correlation between number of tweets and EDILI was similarly moderate, r=0.52 (p-value=0.0049). Correlations to EDILI stratified by age (0-4, 5-17, 18-64, 65+) also showed a moderate positive relationship (range 0.50 to 0.55, all p-values < 0.01). Twitter use peaked one week before the recorded peak of other surveillance indicators. When Twitter counts were shifted one week to align the peak in tweets with the peak of the influenza season, the correlations were 0.54 for LSL and 0.61 for EDILI (p-value=0.0034 and 0.0007, respectively).ConclusionsOverall, the tweets collected had a moderately positive relationship with the severity of influenza activity in Cook County. When the data were transitioned to match peaks, there was an increase in the correlations’ strength for both LSL and EDILI. This data indicates that publicly shared social media data may be an underutilized source of syndromic data at the local level, potentially capable of predicting seasonal influenza peaks before traditional data sources. Other jurisdictions may consider using the open source program created by CCDPH to determine the relationship of influenza related social media to their own local influenza surveillance data. For the 2017-2018 influenza season, we established a redundant system for tweet collection in case one of the systems goes down. Exploring machine learning (in place of manual review) to detect tweets indicating illness is also a promising avenue to simplify data collection and cleaning. Data will be collected using the same system for the 2017-2018 influenza season and correlations re-evaluated with more complete data.

Sentiment analysis can be used to study an individual or a group’s emotions and attitudes towards other people and entities like products, services, or social events. With the advancements in the field of deep learning, the enormity of available information on internet, chiefly on social media, combined with powerful computing machines, it’s just a matter of time before artificial intelligence (AI) systems make their presence in every aspect of human life, making our lives more introspective. In this paper, we propose to implement a multimodal sentiment prediction system that can analyze the emotions predicted from different modal sources such as video, audio and text and integrate them to recognize the group emotions of the students in a classroom. Our experimental setup involves a digital video camera with microphones to capture the live video and audio feeds of the students during a lecture. The students are advised to provide their digital feedback on the lecture as ‘tweets’ on their twitter account addressed to the lecturer’s official twitter account. The audio and video frames are separated from the live streaming video using tools such as lame and ffmpeg. A twitter API was used to access and extract messages from twitter platform. The audio and video features are extracted using Mel-Frequency Cepstral Co-efficients (MFCC) and Haar Cascades classifier respectively. The extracted features are then passed to the Convolutional Neural Network (CNN) model trained on the FER2013 facial images database to generate the feature vector for classification of video-based emotions. A Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), trained on speech emotion corpus database was used to train on the audio features. A lexicon-based approach with senti-word dictionary and learning based approach with custom dataset trained by Support Vector Machines (SVM) was used in the twitter-texts based approach. A decision-level fusion algorithm was applied on these three different modal schemes to integrate the classification results and deduce the overall group emotions of the students. The use-case of this proposed system will be in student emotion recognition, employee performance feedback, monitoring or surveillance-based systems. The implemented system framework was tested in a classroom environment during a live lecture and the predicted emotions demonstrated the classification accuracy of our approach.

Objective: We aim to explore how to effectively leverage social media for vaping electronic cigarette (e-cigarette) surveillance. This study examines how members of a social media platform called Reddit utilize topically-oriented sub-communities for e-cigarette discussions.Introduction: In recent years, individuals have been using social network sites like Facebook, Twitter, and Reddit to discuss health-related topics. These social media platforms consequently became new avenues for research and applications for researchers, for instance disease surveillance. Reddit, in particular, can potentially provide more in-depth contextual insights compared to Twitter, and Reddit members discuss potentially more diverse topics than Facebook members. However, identifying relevant discussions remains a challenge in large datasets like Reddit. Thus, much previous research using Reddit data focused on selected few topically-oriented sub-communities. Although such approach allows for topically focused datasets, a large portion of related data can be missed. In this research, we examine all sub-communities in which members are discussing e-cigarettes in order to determine if investigating these other sub-communities could result in a better smoking surveillance system.Methods: In this study, we use an archived Reddit dataset1 that had been used in previous studies2,3. We first preprocessed the dataset, which included converting text to lower case and removing punctuation. Due to the size of the dataset (114,320,798 posts and 1,659,361,605 associated comments from 239,772 sub-communities), we identified 4 terms to extract posts or comments about e-cigarettes via a lexicon-based approach. The terms are 'e cig', 'elec cig', and 'electronic cig'. We included any partial matches in this process to cover a variation of e-cigarette terms. For example, a partial match of ‘cig’ can cover ‘cig’, ‘cigs’, ‘cigarette’, and ‘cigarettes’. We presented the Wordcloud of the names and frequencies of sub-communities, in which members discussed e-cigarettes.Results: We extracted 354,587 posts/comments that were made by 176,252 unique member IDs from 6,039 unique sub-communities. There were 6 sub-communities with more than 8,000 e-cigarette posts. The sub-communities are ‘AskReddit’ (59,939) ‘Cigars’ (51,684) ‘electronic_cigarette’ (24,393), ‘trees’ (17,752), ‘pics’ (8,792), ‘stopsmoking’ (8,589). Other notable sub-communities are ‘news’ (5,010), ‘politics’ (4,662), ‘worldnews’ (3,785), ‘science’ (3,279), ‘Drugs’ (2,967), ‘PipeTobacco’ (2,099), ‘Cigarettes’ (1,401), ‘teenagers’ (1,016), ‘AskMen’ (918), ‘Marijuana’ (826), ‘Fitness’ (818), ‘AskWomen’ (698), ‘cubancigars’ (695), and ‘vaporents’ (608). Members were participating not only in sub-communities related to smoking and smoking cessation, but also in science, news, health, teenager, and Q&A sub-communities. The overview of the sub-communities that members participated to discuss e-cigarette are summarized in Figure 1.Conclusions: We present preliminary findings concerning the various sub-communities in which members had discussion on e-cigarettes in the popular social media platform Reddit. Our initial results suggest that Reddit members openly discuss electronic cigarette-related issues in many sub-communities that are unrelated to smoking. For the purpose of e-cigarettes surveillance, understanding the discussions in unrelated sub-communities, for example the subreddit ‘teenagers’, can provide opportunities to gain an in-depth perspective on the increased use of e-cigarettes by youth or non-smoker4. Moreover, high levels of activities in Q&A sub-communities like ‘AskReddit’, ‘AskMen’, and ‘AskWomen’ could indicate ineffective information dissemination regarding e-cigarettes5, warranting further investigation. For the purpose of disease surveillance, we conclude that understanding the discussion in unrelated sub-communities has the potential to improve the practice of public health surveillance.

Kite J, Foley BC, Grunseit AC, Freeman B. Please Like Me: Facebook and Public Health Communication. PLoS One 2016;11(9) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162765 Sharpe JD, Hopkins RS, Cook RL, Striley CW. Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis. JMIR Public Health Surveill 2016 20;2(2) http://publichealth.jmir.org/2016/2/e161/ Tran A, Trevennec C, Lutwama J, Sserugga J, Gély M, Pittiglio C, Pinto J, Chevalier V. Development and Assessment of a Geographic Knowledge-Based Model for Mapping Suitable Areas for Rift Valley Fever Transmission in Eastern Africa. PLoS Negl Trop Dis 2016;10(9) http://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0004999

Kite J, Foley BC, Grunseit AC, Freeman B. Please Like Me: Facebook and Public Health Communication. PLoS One 2016;11(9) http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162765 Sharpe JD, Hopkins RS, Cook RL, Striley CW. Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis. JMIR Public Health Surveill 2016 20;2(2) http://publichealth.jmir.org/2016/2/e161/ Tran A, Trevennec C, Lutwama J, Sserugga J, Gély M, Pittiglio C, Pinto J, Chevalier V. Development and Assessment of a Geographic Knowledge-Based Model for Mapping Suitable Areas for Rift Valley Fever Transmission in Eastern Africa. PLoS Negl Trop Dis 2016;10(9) http://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0004999

Human history has witnessed varied surveillance and counter-surveillance activities from time immemorial. Human beings could not surveille others effectively and accurately without the technology of their era. Technology is a tool that can empower both people and governments. The outcomes are different based on the users’ intentions and aims. 2,500 years ago, Sun Tzu noted that ‘If you know both yourself and your enemy, you can win numerous (literally, "a hundred") battles without jeopardy’. His words still ring true. To be a good surveiller and counter-surveiller it is essential to know both sides, and in order to be good at these activities access to technology is vital. There is no doubt that knowledge is power, and without technology to access the information, it is impossible to be powerful. As we become more expert at technology, we will learn what makes surveillance and counter-surveillance more effective, and will be more powerful.“Surveillance” is one of the most important aspects of living in the convergent media environment. This essay illustrates government surveillance and counter-surveillance during the Iranian Green Movement (2009) on social and mobile media. The Green Movement refers to a non-violent movement that arose after the disputed presidential election on June 2009. After that Iran was facing its most serious political crisis since the 1979 revolution. Claims of vote fraud triggered massive street protests. Many took to the streets with “Green” signs, chanting slogans such as ‘the government lied’, and ‘where is my vote?’ There is no doubt that social and mobile media has played an important role in Iran’s contemporary politics. According to Internet World Stats (IWS) Internet users in 2009 account for approximately 48.5 per cent of the population of Iran. In 2009, Iran had 30.2 million mobile phone users (Freedom House), and 72 cellular subscriptions for every 100 people (World Bank). Today, while Iran has the 19th-largest population in the world, its blogosphere holds the third spot in terms of number of users, just behind the United States and China (Beth Elson et al.). In this essay the use of social and mobile media (technology) is not debated, but the extent of this use, and who, why and how it is used, is clearly scrutinised.Visibility and Surveillance There have been different kinds of surveillance for a very long time. However, all types of surveillance are based on the notion of “visibility”. Previous studies show that visibility is not a new term (Foucault Discipline). The new things in the new era, are its scale, scope and complicated ways to watch others without being watched, which are not limited to a specific time, space and group, and are completely different from previous instruments for watching (Andrejevic). As Meikle and Young (146) have mentioned ‘networked digital media bring with them a new kind of visibility’, based on different kinds of technology. Internet surveillance has important implications in politics to control, protect, and influence (Marx Ethics; Castells; Fuchs Critique). Surveillance has been improved during its long history, and evolved from very simple spying and watching to complicated methods of “iSpy” (Andrejevic). To understand the importance of visibility and its relationship with surveillance, it is essential to study visibility in conjunction with the notion of “panopticon” and its contradictory functions. Foucault uses Bentham's notion of panopticon that carries within itself visibility and transparency to control others. “Gaze” is a central term in Bentham’s view. ‘Bentham thinks of a visibility organised entirely around a dominating, overseeing gaze’ (Foucault Eye). Moreover, Thomson (Visibility 11) notes that we are living in the age of ‘normalizing the power of the gaze’ and it is clear that the influential gaze is based on powerful means to see others.Lyon (Surveillance 2) explains that ‘surveillance is any collection and processing of personal data, whether identifiable or not, for the purpose of influencing or managing those whose data have been granted…’. He mentions that today the most important means of surveillance reside in computer power which allows collected data to be sorted, matched, retrieved, processed, marketed and circulated.Nowadays, the Internet has become ubiquitous in many parts of the world. So, the changes in people’s interactions have influenced their lives. Fuchs (Introduction 15) argues that ‘information technology enables surveillance at a distance…in real time over networks at high transmission speed’. Therefore, visibility touches different aspects of people’s lives and living in a “glasshouse” has caused a lot of fear and anxiety about privacy.Iran’s Green Movement is one of many cases for studying surveillance and counter-surveillance technologies in social and mobile media. Government Surveillance on Social and Mobile Media in Iran, 2009 In 2009 the Iranian government controlled technology that allowed them to monitor, track, and limit access to the Internet, social media and mobiles communication, which has resulted in the surveillance of Green Movement’s activists. The Iranian government had improved its technical capabilities to monitor the people’s behavior on the Internet long before the 2009 election. The election led to an increase in online surveillance. Using social media the Iranian government became even more powerful than it was before the election. Social media was a significant factor in strengthening the government’s power. In the months after the election the virtual atmosphere became considerably more repressive. The intensified filtering of the Internet and implementation of more advanced surveillance systems strengthened the government’s position after the election. The Open Net Initiative revealed that the Internet censorship system in Iran is one of the most comprehensive and sophisticated censorship systems in the world. It emphasized that ‘Advances in domestic technical capacity have contributed to the implementation of a centralized filtering strategy and a reduced reliance on Western technologies’.On the other hand, the authorities attempted to block all access to political blogs (Jaras), either through cyber-security methods or through threats (Tusa). The Centre for Investigating Organized Cyber Crimes, which was founded in 2007 partly ‘to investigate and confront social and economic offenses on the Internet’ (Cyber Police), became increasingly important over the course of 2009 as the government combated the opposition’s online activities (Beth Elson et al. 16). Training of "senior Internet lieutenants" to confront Iran's "virtual enemies online" was another attempt that the Intelligence minister announced following the protests (Iran Media Program).In 2009 the Iranian government enacted the Computer Crime Law (Jaras). According to this law the Committee in Charge of Determining Unauthorized Websites is legally empowered to identify sites that carry forbidden content and report that information to TCI and other major ISPs for blocking (Freedom House). In the late fall of 2009, the government started sending threatening and warning text messages to protesters about their presence in the protests (BBC). Attacking, blocking, hacking and hijacking of the domain names of some opposition websites such as Jaras and Kaleme besides a number of non-Iranian sites such as Twitter were among the other attempts of the Iranian Cyber Army (Jaras).It is also said that the police and security forces arrested dissidents identified through photos and videos posted on the social media that many imagined had empowered them. Furthermore, the online photos of the active protesters were posted on different websites, asking people to identify them (Valizadeh).In late June 2009 the Iranian government was intentionally permitting Internet traffic to and from social networking sites such as Facebook and Twitter so that it could use a sophisticated practice called Deep Packet Inspection (DPI) to collect information about users. It was reportedly also applying the same technology to monitor mobile phone communications (Beth Elson et al. 15).On the other hand, to cut communication between Iranians inside and outside the country, Iran slowed down the Internet dramatically (Jaras). Iran also blocked access to Facebook, YouTube, Wikipedia, Twitter and many blogs before, during and after the protests. Moreover, in 2009, text message services were shut down for over 40 days, and mobile phone subscribers could not send or receive text messages regardless of their mobile carriers. Subsequently it was disrupted on a temporary basis immediately before and during key protests days.It was later discovered that the Nokia Siemens Network provided the government with surveillance technologies (Wagner; Iran Media Program). The Iranian government built a complicated system that enabled it to monitor, track and intercept what was said on mobile phones. Nokia Siemens Network confirmed it supplied Iran with the technology needed to monitor, control, and read local telephone calls [...] The product allowed authorities to monitor any communications across a network, including voice calls, text messaging, instant messages, and web traffic (Cellan-Jones). Media sources also reported that two Chinese companies, Huawei and ZTE, provided surveillance technologies to the government. The Nic Payamak and Saman Payamak websites, that provide mass text messaging services, also reported that operator Hamrah Aval commonly blocked texts with words such as meeting, location, rally, gathering, election and parliament (Iran Media Program). Visibility and Counter-Surveillance The panopticon is not limited to the watchers. Similarly, new kinds of panopticon and visibility are not confined to government surveillance. Foucault points out that ‘the seeing machine was once a sort of dark room into which individuals spied; it has become a transparent building in which the exercise of power may be supervised by society as a whole’ (Discipline 207). What is important is Foucault's recognition that transparency, not only of those who are being observed but also of those who are observing, is central to the notion of the panopticon (Allen) and ‘any member of society will have the right to come and see with his own eyes how schools, hospitals, factories, and prisons function’ (Foucault, Discipline 207). Counter-surveillance is the process of detecting and mitigating hostile surveillance (Burton). Therefore, while the Internet is a surveillance instrument that enables governments to watch people, it also improves the capacity to counter-surveille, and draws public attention to governments’ injustice. As Castells (185) notes the Internet could be used by citizens to watch their government as an instrument of control, information, participation, and even decision-making, from the bottom up.With regards to the role of citizens in counter-surveillance we can draw on Jay Rosen’s view of Internet users as ‘the people formerly known as the audience’. In counter-surveillance it can be said that passive citizens (formerly the audience) have turned into active citizens. And this change was becoming impossible without mobile and social media platforms. These new techniques and technologies have empowered people and given them the opportunity to have new identities. When Thompson wrote ‘the exercise of power in modern societies remains in many ways shrouded in secrecy and hidden from the public gaze’ (Media 125), perhaps he could not imagine that one day people can gaze at the politicians, security forces and the police through the use of the Internet and mobile devices.Furthermore, while access to mobile media allows people to hold authorities accountable for their uses and abuses of power (Breen 183), social media can be used as a means of representation, organization of collective action, mobilization, and drawing attention to police brutality and reasons for political action (Gerbaudo).There is no doubt that having creativity and using alternative platforms are important aspects in counter-surveillance. For example, images of Lt. Pike “Pepper Spray Cop” from the University of California became the symbol of the senselessness of police brutality during the Occupy Movement (Shaw). Iranians’ Counter-Surveillance on Social and Mobile Media, 2009 Iran’s Green movement (2009) triggered a lot of discussions about the role of technology in social movements. In this regard, there are two notable attitudes about the role of technology: techno-optimistic (Shriky and Castells) and techno-pessimistic (Morozov and Gladwell) views should be taken into account. While techno-optimists overrated the role of social media, techno-pessimists underestimated its role. However, there is no doubt that technology has played a great role as a counter-surveillance tool amongst Iranian people in Iran’s contemporary politics.Apart from the academic discussions between techno-optimists and techno-pessimists, there have been numerous debates about the role of new technologies in Iran during the Green Movement. This subject has received interest from different corners of the world, including Western countries, Iranian authorities, opposition groups, and also some NGOs. However, its role as a means of counter-surveillance has not received adequate attention.As the tools of counter-surveillance are more or less the tools of surveillance, protesters learned from the government to use the same techniques to challenge authority on social media.Establishing new websites (such as JARAS, RASA, Kalemeh, and Iran green voice) or strengthening some previous ones (such as Saham, Emrooz, Norooz), also activating different platforms such as Facebook, Twitter, and YouTube accounts to broadcast the voice of the Iranian Green Movement and neutralize the government’s propaganda were the most important ways to empower supporters of Iran’s Green Movement in counter-surveillance.‘Reporters Without Borders issued a statement, saying that ‘the new media, and particularly social networks, have given populations collaborative tools with which they can change the social order’. It is also mentioned that despite efforts by the Iranian government to prevent any reporting of the protests and due to considerable pressure placed on foreign journalists inside Iran, social media played a significant role in sending the messages and images of the movement to the outside world (Axworthy). However, at that moment, many thought that Twitter performed a liberating role for Iranian dissenters. For example, Western media heralded the Green Movement in Iran as a “Twitter revolution” fuelled by information and communication technologies (ICTs) and social media tools (Carrieri et al. 4). “The Revolution Will Be Twittered” was the first in a series of blog posts published by Andrew Sullivan a few hours after the news of the protests was released.According to the researcher’s observation the numbers of Twitter users inside Iran who tweeted was very limited in 2009 and social media was most useful in the dissemination of information, especially from those inside Iran to outsiders. Mobile phones were mostly influential as an instrument firstly used for producing contents (images and videos) and secondly for the organisation of protests. There were many photos and videos that were filmed by very simple mobile cell phones, uploaded by ordinary people onto YouTube and other platforms. The links were shared many times on Twitter and Facebook and released by mainstream media. The most frequently circulated story from the Iranian protests was a video of Neda Agha-Sultan. Her final moments were captured by some bystanders with mobile phone cameras and rapidly spread across the global media and the Internet. It showed that the camera-phone had provided citizens with a powerful means, allowing for the creation and instant sharing of persuasive personalised eyewitness records with mobile and globalised target populations (Anden-Papadopoulos).Protesters used another technique, DDOS (distributed denial of service attacks), for political protest in cyber space. Anonymous people used DDOS to overload a website with fake requests, making it unavailable for users and disrupting the sites set as targets (McMillan) in effect, shutting down the site. DDOS is an important counter-surveillance activity by grassroots activists or hackers. It was a cyber protest that knocked the main Iranian governmental websites off-line and caused crowdsourcing and false trafficking. Amongst them were Mahmoud Ahmadinejad, Iran's supreme leader’s websites and those which belong to or are close to the government or security forces, including news agencies (Fars, IRNA, Press TV…), the Ministry of Foreign Affairs, the Ministry of Justice, the Police, and the Ministry of the Interior.Moreover, as authorities uploaded the pictures of protesters onto different platforms to find and arrest them, in some cities people started to put the pictures, phone numbers and addresses of members of security forces and plain clothes police officers who attacked them during the protests and asked people to identify and report the others. They also wanted people to send information about suspects who infringed human rights. Conclusion To sum up, visibility, surveillance and counter-surveillance are not new phenomena. What is new is the technology, which increased their complexity. As Foucault (Discipline 200) mentioned ‘visibility is a trap’, so being visible would be the weakness of those who are being surveilled in the power struggle. In the convergent era, in order to be more powerful, both surveillance and counter-surveillance activities aim for more visibility. Although both attempt to use the same means (technology) to trap the other side, the differences are in their subjects, objects, goals and results.While in surveillance, visibility of the many by the few is mostly for the purpose of control and influence in undemocratic ways, in counter-surveillance, the visibility of the few by the many is mostly through democratic ways to secure more accountability and transparency from the governments.As mentioned in the case of Iran’s Green Movement, the scale and scope of visibility are different in surveillance and counter-surveillance. The importance of what Shaw wrote about Sydney occupy counter-surveillance, applies to other places, such as Iran. She has stressed that ‘protesters and police engaged in a dance of technology and surveillance with one another. Both had access to technology, but there were uncertainties about the extent of technology and its proficient use…’In Iran (2009), both sides (government and activists) used technology and benefited from digital networked platforms, but their levels of access and domains of influence were different, which was because the sources of power, information and wealth were divided asymmetrically between them. Creativity was important for both sides to make others more visible, and make themselves invisible. Also, sharing information to make the other side visible played an important role in these two areas. References Alen, David. “The Trouble with Transparency: The Challenge of Doing Journalism Ethics in a Surveillance Society.” Journalism Studies 9.3 (2008): 323-40. 8 Dec. 2013 ‹http://www.tandfonline.com/doi/full/10.1080/14616700801997224#.UqRFSuIZsqN›. Anden-Papadopoulos, Kari. “Citizen Camera-Witnessing: Embodied Political Dissent in the Age of ‘Mediated Mass Self-Communication.’” New Media & Society 16.5 (2014). 753-69. 9 Aug. 2014 ‹http://nms.sagepub.com/content/16/5/753.full.pdf+html›. Andrejevic, Mark. iSpy: Surveillance and Power in the Interactive Era. Lawrence, Kan: UP of Kansas, 2007. Axworthy, Micheal. Revolutionary Iran: A History of the Islamic Republic. London: Penguin Books, 2014. Bentham, Jeremy. Panopticon Postscript. London: T. Payne, 1791. Beth Elson, Sara, Douglas Yeung, Parisa Roshan, S.R. Bohandy, and Alireza Nader. Using Social Media to Gauge Iranian Public Opinion and Mood after the 2009 Election. Santa Monica: RAND Corporation, 2012. 1 Aug. 2014 ‹http://www.rand.org/content/dam/rand/pubs/technical_reports/2012/RAND_TR1161.pdf›. Breen, Marcus. Uprising: The Internet’s Unintended Consequences. Champaign, Ill: Common Ground Pub, 2011. Burton, Fred. “The Secrets of Counter-Surveillance.” Stratfor Global Intelligence. 2007. 19 April 2015 ‹https://www.stratfor.com/secrets_countersurveillance›. Carrieri, Matthew, Ali Karimzadeh Bangi, Saad Omar Khan, and Saffron Suud. After the Green Movement Internet Controls in Iran, 2009-2012. OpenNet Initiative, 2013. 17 Dec. 2013 ‹https://opennet.net/sites/opennet.net/files/iranreport.pdf›. Castells, Manuel. The Internet Galaxy: Reflections on the Internet, Business, and Society. Oxford: Oxford UP: 2001. Cellan-Jones, Rory. “Hi-Tech Helps Iranian Monitoring.” BBC, 2009. 26 July 2014 ‹http://news.bbc.co.uk/1/hi/technology/8112550.stm›. “Cyber Crimes’ List.” Iran: Cyber Police, 2009. 17 July 2014 ‹http://www.cyberpolice.ir/page/2551›. Foucault, Michel. Discipline and Punish: The Birth of the Prison. Trans. Alan Sheridan. Harmondsworth: Penguin, 1977. Foucault, Michel. “The Eye of Power.” 1980. 12 Dec. 2013 ‹https://nbrokaw.files.wordpress.com/2010/12/the-eye-of-power.doc›. Freedom House. “Special Report: Iran.” 2009. 14 June 2014 ‹http://www.sssup.it/UploadDocs/4661_8_A_Special_Report_Iran_Feedom_House_01.pdf›. Fuchs, Christian. “Introduction.” Internet and Surveillance: The Challenges of Web 2.0 and Social Media. Ed. Christian Fuchs. London: Routledge, 2012. 1-28. Fuchs, Christian. “Critique of the Political Economy of Web 2.0 Surveillance.” Internet and Surveillance: The Challenges of Web 2.0 and Social Media. Ed. Christian Fuchs. London: Routledge, 2012. 30-70. Gerbaudo, Paolo. Tweets and the Streets: Social Media and Contemporary Activism. London: Pluto, 2012. “Internet: Iran’s New Imaginary Enemy.” Jaras Mar. 2009. 28 June 2014 ‹http://www.rahesabz.net/print/12143›.Iran Media Program. “Text Messaging as Iran's New Filtering Frontier.” 2013. 25 July 2014 ‹http://www.iranmediaresearch.org/en/blog/227/13/04/25/136›. Internet World Stats News. The Internet Hits 1.5 Billion. 2009. 3 July 2014 ‹ http://www.internetworldstats.com/pr/edi038.htm›. Lyon, David. Surveillance Society: Monitoring Everyday Life. Buckingham: Open UP, 2001. Lyon, David. “9/11, Synopticon, and Scopophilia: Watching and Being Watched.” The New Politics of Surveillance and Visibility. Eds. Richard V. Ericson and Kevin D. Haggerty. Toronto: UP of Toronto, 2006. 35-54. Marx, Gary T. “What’s New about the ‘New Surveillance’? Classify for Change and Continuity.” Surveillance & Society 1.1 (2002): 9-29. McMillan, Robert. “With Unrest in Iran, Cyber-Attacks Begin.” PC World 2009. 17 Apr. 2015 ‹http://www.pcworld.com/article/166714/article.html›. Meikle, Graham, and Sherman Young. Media Convergence: Networked Digital Media in Everyday Life. London: Palgrave Macmillan, 2012. Morozov, Evgeny. “How Dictators Watch Us on the Web.” Prospect 2009. 15 June 2014 ‹http://www.prospectmagazine.co.uk/magazine/how-dictators-watch-us-on-the-web/#.U5wU6ZRdU00›.Open Net. “Iran.” 2009. 26 June 2014 ‹https://opennet.net/research/profiles/iran›. Reporters without Borders. “Web 2.0 versus Control 2.0.” 2010. 27 May 2014 ‹http://en.rsf.org/web-2-0-versus-control-2-0-18-03-2010,36697›.Rosen, Jay. The People Formerly Known as the Audience. 2006. 7 Dec. 2013 ‹http://www.huffingtonpost.com/jay-rosen/the-people-formerly-known_1_b_24113.html›. Shaw, Frances. “'Walls of Seeing': Protest Surveillance, Embodied Boundaries, and Counter-Surveillance at Occupy Sydney.” Transformation 23 (2013). 9 Dec. 2013 ‹http://www.transformationsjournal.org/journal/issue_23/article_04.shtml›. “The Warning of the Iranian Revolutionary Guard Corps (IRGC) to the Weblogs and Websites.” BBC, 2009. 27 July 2014 ‹http://www.bbc.co.uk/persian/iran/2009/06/090617_ka_ir88_sepah_internet.shtml›. Thompson, John B. The Media And Modernity: A Social Theory of the Media. Cambridge: Polity Press, 1995. Thompson, John B. “The New Visibility.” Theory, Culture & Society 22.6 (2005): 31-51. 10 Dec. 2013 ‹http://tcs.sagepub.com/content/22/6/31.full.pdf+html›. 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This article analytically describes the digital technologies-embedded repression practices developed against a local grassroot environmental protest in Far Northern Russia. Unlike urban political opposition that uses United States-based social media platforms (Facebook and Twitter), grassroots movements mainly use VKontakte, the Russia-developed dominant social network in the country. They use it despite the potential privacy and security risks this platform has posed to users since 2014. By means of an ethnographic approach, this article focuses on government responses to online protest activities and counter-practices formulated by activists to circumvent limitations. Inhabitants have been fighting since July 2018 against a waste landfill project designed to ship vast quantities of garbage from Moscow to a remote site called Shies. A protest camp was set up and maintained to physically preserve the site, joined by people from all over Russia. This article shows that, even as it became a target of government surveillance, VKontakte remains a crucial tool for local activism.

ObjectiveWe develop new spatial scan models that use individuals' movement data, rather than a single location per individual, in order to identify areas with a high relative risk of infection by dengue disease.IntroductionTraditionally, surveillance systems for dengue and other infectious diseases locate each individual case by home address, aggregate these locations to small areas, and monitor the number of cases in each area over time. However, human mobility plays a key role in dengue transmission, especially due to the mosquito day-biting habit,1 and relying solely on individuals’ residential address as a proxy for dengue infection ignores a multitude of exposures that individuals are subjected to during their daily routines. Residence locations may be a poor indicator of the actual regions where humans and infected vectors tend to interact more, and hence, provide little information for dengue prevention. The increasing availability of geolocated data in online platforms such as Twitter offers a unique opportunity: in addition to identifying diseased individuals based on the textual content, we can also follow them in time and space as they move on the map and model their movement patterns. Comparing the observed mobility patterns for case and control individuals can provide relevant information to detect localized regions with higher risk of dengue infection. Incorporating the mobility of individuals into risk modeling requires the development of new spatial models that can cope with this type of data in a principled way and efficient algorithms to deal with the ever-growing amount of data. We propose new spatial scan models and exploit geo-located data from Twitter to detect geographic clusters of dengue infection risk.MethodsAs the spatial tracking of a large sample of infected and non-infected individuals is expensive and raises serious privacy issues, we instead analyze geo-located Twitter data (tweets), which is readily and publicly available. We identify “infected” individuals (cases) as those individuals who have at least one tweet classified as a current, personal experience with dengue. We note that, because of the incubation period and recovery time, infected Twitter users are likely to mention dengue in their tweets days after they are infected, and usually not at the location where the exposure (mosquito bite) occurred. Once we have identified cases and controls based on the textual content of the messages, we then compare the mobility patterns of the two groups. The key aspect of our method is that the input is a series of locations rather than a single location, such as the residence address, for each individual. The number of positions ni composing each mobility pattern can vary substantially between individuals i, and thus simple approaches like counting the total numbers of case and control tweets per location would be biased and inaccurate; moreover, individuals with larger numbers of tweets may be more likely to be identified as a case. Nevertheless, our assumption is that the entire mobility patterns will be informative of the riskier areas if we compare the spatial patterns from infected and non-infected individuals.We have developed two new spatial scan methods (unconditional and conditional spatial logistic models) which correctly account for the multiple, varying number of spatial locations per individual. Both models use the proportion of an individual’s tweets in each location as an estimate of the proportion of time spent in that location; the estimate is biased by individuals’ propensity to tweet in different locations, but is expected to capture the large amounts of time spent at frequently visited locations. Our unconditional model controls the variable contribution of each individual through a non-parametric estimation of the odds of being a case and has a semi-parametric logistic specification. When estimating the previous offset becomes a complex task, we propose a case-control matching strategy in the conditional model to control for the number of tweets ni. Based on the subset scan approach,3 we search for localized regions where the infection risk is substantially higher than in the rest of the map by maximizing a log-likelihood ratio statistic over subsets of the data.ResultsWe demonstrate the detection of high-risk clusters for dengue infection using Twitter data we collected in Brazil during the year of 2015, when a strong surge of dengue hit several cities. We apply our method to the cities with highest number of case individuals. There are many points of interest, such as hospitals and parks, inside the detected regions. As those places are non-residential, standard approaches would fail to consider them as potential infection places in the event of a spike in the number of cases. Figure 1 shows the detected regions in the city of Campinas, Brazil. Synthetic and real-world evaluation results demonstrate that our methods work better than either just mapping each individual to their most frequent location (which is a proxy for home address) and running a traditional spatial scan, or scanning using tweet volume as an input.ConclusionsIdentifying places where people have higher risk of being infected, rather than focusing on residential address locations, may be key to surveillance for vector-borne diseases such as malaria and dengue, allowing public health officials to focus mitigation actions. The stochasticity of location data is not appropriate for typical spatial cluster detection tools such as the traditional spatial scan statistic.2 Each user is represented by a different number of geographic points and the variability of these numbers is large; traditional approaches can be easily misled if not extended to account for this special structure. Dengue is just one of many infectious diseases with a well-known etiology but a huge number of uncertain and difficult to obtain parameters that quantify factors such as infected mosquito population, likelihood of being bitten by an infected mosquito, and human movement in the mosquito-infested areas. Our methods add to the set of tools that spatial epidemiologists have available to search for spatially localized risk clusters using readily available Twitter data. We expect that our method will also be useful to other public health surveillance problems where movement data can bring relevant information.References1. Stoddard, ST., et al. The role of human movement in the transmission of vector-borne pathogens. PLOS NTDS. 2009; 3 (7): 1–92. Kulldorff M. A spatial scan statistic. Commun Stat Theory Methods. 1997; 26(2): 1481-14963. Neill DB. Fast subset scan for spatial pattern detection. J. Royal Stat. Soc. B. 2012; 74(2): 337-360

Social media platforms have become ubiquitous and allow users to share information in real-time. Our study uses data analytics as an approach to explore non-communicable diseases on social media platforms and to identify trends and patterns of related disease symptoms. Exploring epidemiological patterns of non-communicable diseases is vital given that they have become prevalent in low-income communities, accounting for about 38 million deaths worldwide. We collected data related to multiple disease conditions from the Twitter microblogging platform and zoomed into symptoms related to heart diseases. As part of our analyses, we focussed on the mechanism and trends of disease occurrences. Our results show that specific symptoms may be attributed to multiple disease conditions and it is viable to identify trends and patterns of their occurrences using a structured analytics approach. This can then act as a supplementary tool to support epidemiological initiatives that monitor non-communicable diseases. Based on the study’s results, we identify that non-communicable disease surveillance approach using social media analytics could support the design of effective health intervention strategies.