Relevant bibliographies by topics / Twitter-based Surveillance

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Academic literature on the topic 'Twitter-based Surveillance'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Twitter-based Surveillance"

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Tsai, Meng Hsiu, and Yingfeng Wang. "Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19." International Journal of Environmental Research and Public Health 18, no. 12 (June 10, 2021): 6272. http://dx.doi.org/10.3390/ijerph18126272.

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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.
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Wakamiya, Shoko, Mizuki Morita, Yoshinobu Kano, Tomoko Ohkuma, and Eiji Aramaki. "Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations." Journal of Medical Internet Research 21, no. 2 (February 20, 2019): e12783. http://dx.doi.org/10.2196/12783.

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Gandhi, Nupoor, Alex Morales, Sally Man-Pui Chan, Dolores Albarracin, and ChengXiang Zhai. "Predicting Opioid Overdose Crude Rates with Text-Based Twitter Features (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13787–88. http://dx.doi.org/10.1609/aaai.v34i10.7165.

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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.
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Fuchs, Christian. "Web 2.0, Prosumption, and Surveillance." Surveillance & Society 8, no. 3 (September 2, 2010): 288–309. http://dx.doi.org/10.24908/ss.v8i3.4165.

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“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.
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Daughton, Ashlynn R., Rumi Chunara, and Michael J. Paul. "Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study." JMIR Public Health and Surveillance 6, no. 2 (April 24, 2020): e14986. http://dx.doi.org/10.2196/14986.

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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.
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Harisanty, Dessy. "How Twitter affects academic library users." Library Hi Tech News 35, no. 3 (May 8, 2018): 13–15. http://dx.doi.org/10.1108/lhtn-11-2017-0080.

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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.
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Visweswaran, Shyam, Jason B. Colditz, Patrick O’Halloran, Na-Rae Han, Sanya B. Taneja, Joel Welling, Kar-Hai Chu, Jaime E. Sidani, and Brian A. Primack. "Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study." Journal of Medical Internet Research 22, no. 8 (August 12, 2020): e17478. http://dx.doi.org/10.2196/17478.

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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.
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Reuter, Katja, Yifan Zhu, Praveen Angyan, NamQuyen Le, Akil A. Merchant, and Michael Zimmer. "Public Concern About Monitoring Twitter Users and Their Conversations to Recruit for Clinical Trials: Survey Study." Journal of Medical Internet Research 21, no. 10 (October 30, 2019): e15455. http://dx.doi.org/10.2196/15455.

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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.
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Majmundar, Anuja, Jon-Patrick Allem, Tess Boley Cruz, and Jennifer B. Unger. "Where Do People Vape? Insights from Twitter Data." International Journal of Environmental Research and Public Health 16, no. 17 (August 23, 2019): 3056. http://dx.doi.org/10.3390/ijerph16173056.

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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.
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Baumgartner, Peter, and Nicholas Peiper. "Utilizing Big Data and Twitter to Discover Emergent Online Communities of Cannabis Users." Substance Abuse: Research and Treatment 11 (January 1, 2017): 117822181771142. http://dx.doi.org/10.1177/1178221817711425.

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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.
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Dissertations / Theses on the topic "Twitter-based Surveillance"

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Tuli, Gaurav. "Modeling and Twitter-based Surveillance of Smoking Contagion." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/64426.

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Nicotine, in the form of cigarette smoking, chewing tobacco, and most recently as vapor smoking, is one of the most heavily used addictive drugs in the world. Since smoking imposes a significant health-care and economic burden on the population, there have been sustained and significant efforts for the past several decades to control it. However, smoking epidemic is a complex and "policy-resistant" problem that has proven difficult to control. Despite the known importance of social networks in the smoking epidemic, there has been no network-centric intervention available for controlling the smoking epidemic yet. The long-term goal of this work is the development and implementation of an environment needed for developing network-centric interventions for controlling the smoking contagion. In order to develop such an environment we essentially need: an operationalized model of smoking that can be simulated, to determine the role of online social networks on smoking behavior, and actual methods to perform network-centric interventions. The objective of this thesis is to take first steps in all these categories. We perform Twitter-based surveillance of smoking-related tweets, and use mathematical modeling and simulation techniques to achieve our objective. Specifically, we use Twitter data to infer sentiments on smoking and electronic cigarettes, to estimate the proportion of user population that gets exposed to smoking-related messaging that is underage, and to identify statistically anomalous clusters of counties where people discuss about electronic cigarette a lot more than expected. In other work, we employ mathematical modeling and simulation approach to study how different factors such as addictiveness and peer-influence together contribute to smoking behavior diffusion, and also develop two methods to stymie social contagion. This lead to a total of four smoking contagion-related studies. These studies are just a first step towards the development of a network-centric intervention environment for controlling smoking contagion, and also to show that such an environment is realizable.
Ph. D.
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Jayawardhana, Udaya Kumara. "An ontology-based framework for formulating spatio-temporal influenza (flu) outbreaks from twitter." Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1465941275.

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Book chapters on the topic "Twitter-based Surveillance"

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Bere, Wend-Panga Régis Cédric, Gaoussou Camara, Sadouanouan Malo, Sylvie Despres, Moussa Lo, and Stanislas Ouaro. "Extraction of Relevant Data from Social Media Based on Termino-Ontological Resources: Application to Meningitis Surveillance via Twitter." In Innovations and Interdisciplinary Solutions for Underserved Areas, 52–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51051-0_4.

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Conference papers on the topic "Twitter-based Surveillance"

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Comito, Carmela, Agostino Forestiero, and Clara Pizzuti. "Twitter-based Influenza Surveillance." In the 22nd International Database Engineering & Applications Symposium. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3216122.3216128.

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Gomide, Janaína, Adriano Veloso, Wagner Meira, Virgílio Almeida, Fabrício Benevenuto, Fernanda Ferraz, and Mauro Teixeira. "Dengue surveillance based on a computational model of spatio-temporal locality of Twitter." In the 3rd International Web Science Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2527031.2527049.

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Dai, Xiangfeng, Marwan Bikdash, and Bradley Meyer. "From social media to public health surveillance: Word embedding based clustering method for twitter classification." In SoutheastCon 2017. IEEE, 2017. http://dx.doi.org/10.1109/secon.2017.7925400.

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Kumar, K. N. Pavan, and Marina L. Gavrilova. "Personality Traits Classification on Twitter." In 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2019. http://dx.doi.org/10.1109/avss.2019.8909839.

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