Relevant bibliographies by topics / Online social data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Online social data'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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Journal articles on the topic "Online social data"

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Phithakkitnukoon, Santi. "Sensing Urban Social Geography Using Online Social Networking Data." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 3 (August 3, 2021): 36–39. http://dx.doi.org/10.1609/icwsm.v5i3.14213.

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Growing pool of public-generated bits like online social networking data provides possibility to sense social dynamics in the urban space. In this position paper, we use a location-based online social networking data to sense geo-social activity and analyze the underlying social activity distribution of three different cities: London, Paris, and New York. We find a non-linear distribution of social activity, which follows the Power Law decay function. We perform inter-urban analysis based on social activity distribution and clustering. We believe that our study sheds new light on context-aware urban computing and social sensing.
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Wilk, Violetta, Geoffrey N. Soutar, and Paul Harrigan. "Tackling social media data analysis." Qualitative Market Research: An International Journal 22, no. 2 (April 8, 2019): 94–113. http://dx.doi.org/10.1108/qmr-01-2017-0021.

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PurposeThis paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication.Design/methodology/approachThis study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented.FindingsThis research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA.Research limitations/implicationsQualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.Practical implicationsThe paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data.Originality/valueThis paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.
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Giglietto, Fabio, and Luca Rossi. "Limiti e possibilità degli online social data." SOCIOLOGIA DELLA COMUNICAZIONE, no. 49 (September 2015): 9–18. http://dx.doi.org/10.3280/sc2015-049002.

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Binder, Jens F., Sarah L. Buglass, Lucy R. Betts, and Jean D. M. Underwood. "Online social network data as sociometric markers." American Psychologist 72, no. 7 (October 2017): 668–78. http://dx.doi.org/10.1037/amp0000052.

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Sathya, R., and A. Aruna devi. "Data Mining and Analysis of Online Social Networks." International Journal of Business Intelligents 004, no. 001 (June 15, 2015): 18–21. http://dx.doi.org/10.20894/ijbi.105.004.001.005.

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Alim, Sophia. "Automated Data Extraction from Online Social Network Profiles." International Journal of Virtual Communities and Social Networking 5, no. 4 (October 2013): 24–42. http://dx.doi.org/10.4018/ijvcsn.2013100102.

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As the use of online social networking (OSN) sites is increasing, data extraction from OSN profiles is providing researchers with a rich source of data. Data extraction is divided into non-automated and automated approaches. However, researchers face a variety of ethical challenges especially using automated data extraction approaches. In social networking, there has been a lack of research that looks into the unique ethical challenges of using automated data extraction compared to non-automated extraction. This article explores the history of social research ethics and the unique ethical challenges associated with using automated data extraction, as well as how these impact the researcher. The author's review has highlighted that researchers face challenges when designing an experiment involving automated extraction from OSN profiles due to issues such as extraction methods, the speed at which the field of social media is moving and a lack of information on how to deal with ethical challenges.
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ZHOU, Jingya, Jianxi FAN, and Jin WANG. "Data placement approach for scalable online social networks." SCIENTIA SINICA Informationis 48, no. 3 (February 13, 2018): 329–48. http://dx.doi.org/10.1360/n112017-00064.

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Li, Na, Nan Zhang, and Sajal Das. "Preserving Relation Privacy in Online Social Network Data." IEEE Internet Computing 15, no. 3 (May 2011): 35–42. http://dx.doi.org/10.1109/mic.2011.26.

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Hoque, Imranul, and Indranil Gupta. "Disk Layout Techniques for Online Social Network Data." IEEE Internet Computing 16, no. 3 (May 2012): 24–36. http://dx.doi.org/10.1109/mic.2012.40.

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Verbraken, Thomas, Frank Goethals, Wouter Verbeke, and Bart Baesens. "Predicting online channel acceptance with social network data." Decision Support Systems 63 (July 2014): 104–14. http://dx.doi.org/10.1016/j.dss.2013.08.011.

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Dissertations / Theses on the topic "Online social data"

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Rahman, Mahmudur. "Data Verifications for Online Social Networks." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2299.

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Social networks are popular platforms that simplify user interaction and encourage collaboration. They collect large amounts of media from their users, often reported from mobile devices. The value and impact of social media makes it however an attractive attack target. In this thesis, we focus on the following social media vulnerabilities. First, review centered social networks such as Yelp and Google Play have been shown to be the targets of significant search rank and malware proliferation attacks. Detecting fraudulent behaviors is thus paramount to prevent not only public opinion bias, but also to curb the distribution of malware. Second, the increasing use of mobile visual data in news networks, authentication and banking applications, raises questions of its integrity and credibility. Third, through proof-of- concept implementations, we show that data reported from wearable personal trackers is vulnerable to a wide range of security and privacy attacks, while off-the-shelves security solutions do not port gracefully to the constraints introduced by trackers. In this thesis we propose novel solutions to address these problems. First, we introduce Marco, a system that leverages the wealth of spatial, temporal and network information gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Second, we propose FairPlay, a system that correlates review activities, linguistic and behavioral signals gleaned from longitudinal app data, to identify not only search rank fraud but also malware in Google Play, the most popular Android app market. Third, we describe Movee, a motion sensor based video liveness verification system, that analyzes the consistency between the motion inferred from the simultaneously and independently captured camera and inertial sensor streams. Finally, we devise SensCrypt, an efficient and secure data storage and communication protocol for affordable and lightweight personal trackers. We provide the correctness and efficacy of our solutions through a detailed theoretic and experimental analysis.
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Hong, Dan. "Sharing private data in online social networks /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20HONG.

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Xu, Hailu. "Efficient Spam Detection across Online Social Networks." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658.

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Li, Jingxuan. "Mining the Online Social Network Data: Influence, Summarization, and Organization." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1241.

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Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.
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Fan, Xiaoguang, and 樊晓光. "Study of social-network-based information propagation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899600.

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Information propagation has attracted increasing attention from sociologists, marketing researchers and Information Technology entrepreneurs. With the rapid developments in online and mobile social applications like Facebook, Twitter, and LinkedIn, large-scale, high-speed and instantaneous information dissemination becomes possible, spawning tremendous opportunities for electronic commerce. It is non-trivial to make an accurate analysis on how information is propagated due to the uncertainty of human behavior and the complexity of the social environment. This dissertation is concerned with exploring models, formulations, and heuristics for the social-network-based information propagation process. It consists of three major parts: information diffusion through online social network, modeling social influence propagation, and social-network-based information spreading in opportunistic mobile networks. Firstly, I consider the problem of maximizing the influence propagation through online social networks. To solve it, I introduce a probabilistic maximum coverage problem, and propose a cluster-based heuristic and a neighbor-removal heuristic for two basic diffusion models, namely, the Linear Threshold Model and the Independent Cascade Model, respectively. Realizing that the selection of influential nodes is mainly based on the accuracy and efficiency in estimating the social influence, I build a framework of up-to-2-hop hierarchical network to approximate the spreading of social influence, and further propose a hierarchy-based algorithm to solve the influence maximization problem. Our heuristic is proved to be efficient and robust with competitive performance, low computation cost, and high scalability. The second part explores the modeling on social influence propagation. I develop an analytical model for the influence propagation process based on discrete-time Markov chains, and deduce a close-form equation to express the n-step transition probability matrix. We show that given any initial state the probability distribution of the converged network state could be easily obtained by calculating a matrix product. Finally, I study the social-network-based information spreading in opportunistic mobile networks by analyzing the opportunistic routing process. I propose three social-network-based communication pattern models and utilize them to evaluate the performance of different social-network-based routing protocols based on several human mobility traces. Moreover, I discuss the fairness evaluation in opportunistic routing, and propose a fair packet forwarding strategy which operates as a plugin for traditional social- network-based routing protocols. My strategy improves the imbalance of success rates among users while maintaining approximately the same system throughput.
published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
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Ahmad, Waqar, and Asim Riaz. "Predicting Friendship Levels in Online Social Networks." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3351.

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Abstract Context: Online social networks such as Facebook, Twitter, and MySpace have become the preferred interaction, entertainment and socializing facility on the Internet. However, these social network services also bring privacy issues in more limelight than ever. Several privacy leakage problems are highlighted in the literature with a variety of suggested countermeasures. Most of these measures further add complexity and management overhead for the user. One ignored aspect with the architecture of online social networks is that they do not offer any mechanism to calculate the strength of relationship between individuals. This information is quite useful to identify possible privacy threats. Objectives: In this study, we identify users’ privacy concerns and their satisfaction regarding privacy control measures provided by online social networks. Furthermore, this study explores data mining techniques to predict the levels/intensity of friendship in online social networks. This study also proposes a technique to utilize predicted friendship levels for privacy preservation in a semi-automatic privacy framework. Methods: An online survey is conducted to analyze Facebook users’ concerns as well as their interaction behavior with their good friends. On the basis of survey results, an experiment is performed to justify practical demonstration of data mining phases. Results: We found that users are concerned to save their private data. As a precautionary measure, they restrain to show their private information on Facebook due to privacy leakage fears. Additionally, individuals also perform some actions which they also feel as privacy vulnerability. This study further identifies that the importance of interaction type varies while communication. This research also discovered, “mutual friends” and “profile visits”, the two non-interaction based estimation metrics. Finally, this study also found an excellent performance of J48 and Naïve Bayes algorithms to classify friendship levels. Conclusions: The users are not satisfied with the privacy measures provided by the online social networks. We establish that the online social networks should offer a privacy mechanism which does not require a lot of privacy control effort from the users. This study also concludes that factors such as current status, interaction type need to be considered with the interaction count method in order to improve its performance. Furthermore, data mining classification algorithms are tailor-made for the prediction of friendship levels.
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Pochet, Gilberto Flores. "Analysis of online virtual environments using Data Mining and social networks." reponame:Repositório Institucional da UFABC, 2015.

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Berg, Marcus. "Evaluating Quality of Online Behavior Data." Thesis, Stockholms universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-97524.

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This thesis has two purposes; emphasizing the importance of data quality of Big Data, and identifying and evaluating potential error sources in JavaScript tracking (a client side on - site online behavior clickstream data collection method commonly used in web analytics). The importance of data quality of Big Data is emphasized through the evaluation of JavaScript tracking. The Total Survey Error framework is applied to JavaScript tracking and 17 nonsampling error sources are identified and evaluated. The bias imposed by these error sources varies from large to small, but the major takeaway is the large number of error sources actually identified. More work is needed. Big Data has much to gain from quality work. Similarly, there is much that can be done with statistics in web analytics.
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Sang, Lin. "Social Big Data and Privacy Awareness." Thesis, Uppsala universitet, Institutionen för informatik och media, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242444.

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Based on the rapid development of Big Data, the data from the online social network becomea major part of it. Big data make the social networks became data-oriented rather than social-oriented. Taking this into account, this dissertation presents a qualitative study to research howdoes the data-oriented social network affect its users’ privacy management for nowadays. Within this dissertation, an overview of Big Data and privacy issues on the social network waspresented as a background study. We adapted the communication privacy theory as a frameworkfor further analysis how individuals manage their privacy on social networks. We study socialnetworks as an entirety in this dissertation. We selected Facebook as a case study to present theconnection between social network, Big Data and privacy issues. The data that supported the result of this dissertation collected by the face-to-face and in-depthinterview study. As consequence, we found that the people divided the social networks intodifferent level of openness in order to avoid the privacy invasions and violations, according totheir privacy concern. They reduced and transferred their sharing from an open social networkto a more close one. However, the risk of privacy problems actually raised because peopleneglected to understand the data process on social networks. They focused on managed theeveryday sharing but too easily allowed other application accessed their personal data on thesocial network (such like the Facebook profile).
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Manalo, Cornejo Darryl, and Ali Sabet. "Online Social Lookup: A Study of a Future Employment Tool." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186402.

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From the days of Antonio Meucci and Alexander Graham Bell when the telephone was invented, people have been looking for ways to communicate with each other through the use of technology. With the introduction of the Internet new possibilities were created to communicate with other persons around the world. As more and more individuals got access to the Internet more and more data got sent through the network. As with any platform where number of individuals keeps growing, business is to be made. Different companies offer different types of services, and many of these companies are niched in services where people in some way interact with each other. Social communities are some of the largest websites that are used and on these websites a lot of data is being shared. Pictures, life stories and interests are shared and this data is something that advertisement companies pay money to obtain. We can see this since a large part of revenues from social communities come from ad companies. Since these websites save data about individuals one could collect this data to sum up how an individual act online. In this thesis, we wanted to see if there was any interest in collecting this type of data to develop a business model where the data of individuals would be sold to a third party, and how individuals feel about this type data collection. To get an understanding of what type of data that can be collected, different companies that gather data were looked into. To get an understanding if this business model is something wanted recruiters of different IT companies were contacted for interviews. Focus groups and surveys were used to see how individuals close to their college graduation feel about data collection and the business model that is under construction. From the data collected, we saw that recruiters were not interested in a business model that gathered personal data, but rather professional data such as education, projects and other job related facts that can prove what the job applicant have done. And from the public we got the response that data collection of this sort is not something that is desirable. With the data collected we saw that our business model did not fit our targeted audience, but rather that a modified business model aimed on professional data rather than social data is something that could be developed.
Ända sedan den dagen telefonen skapades av Antonio Meucci och Alexander Graham Bell har människan letat efter nya sätt att kommunicera med varandra via teknologin som finns idag. Internet har introducerade nya sätt att dela olika typer av data världen över. Varje dag får fler och fler människor tillgång till internet det betyder då också att mer data skickas via nätet. Som med alla plattform där antalet individer växer skapas då nya affärsmodeller. Olika företag erbjuder olika typer av tjänster och många av dessa företag fördjupar sig inom kommunikationssektorn så att människor kan integrera med varandra. Socialmedia är bland de populäraste webbsidorna idag och här kan användarna dela data och information med varandra. Dessa data är viktiga för annonseringsföretagen då de vill rikta rätt reklam till användarna. Detta ser vi nu eftersom sociala mediernas största inkomstkälla kommer ifrån säljandet av data till annonsering bolagen. Man skulle kunna ta all data som dessa företag har sparat på sina användare för att sammanställa hur de använder tjänsten. I vår rapport ville vi se om det dann något intresse för att samla in denna typ av data för att utveckla vår affärsmodell där individens data och information säljes till en tredje part. Vi ville även undersöka hur användaren känner när det gäller datasamling på internet. För att få en uppfattning på vad för data som kan samlas in på internet har vi undersökt två företag för att se vad för data de tar. När det gäller vår affärsmodell har vi kontaktat och intervjuat rekryterare från olika företag för att se om vår affärsmodell är något som de behöver. Focused Groups och enkäter skickades ut till studenter som nästan har sin examen för att höra vad de har för åsikt är gällande datainsamling och vår affärsmodell. Vår undersökning visade att datainsamling inte var eftertraktad, men de ville däremot samla kompetens information istället. Information så som utbildning, projekt och arbetskarriär. Enkäten och Focused Groups visade även där att personlig datainsamling inte var något som de ville ha. Med de data vi fått under vår undersökning tydde det på att vår affärside inte var riktad mot rätt målgrupp, men en justering av vår affärsmodell i form av datainsamling av kompetens information var något de ville ha.
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Books on the topic "Online social data"

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Morzy, Mikołaj. Mining social-driven data. Poznań: Wydawn. Politechnika Poznańska, 2009.

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Morzy, Mikołaj. Mining social-driven data. Poznań: Wydawn. Politechnika Poznańska, 2009.

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service), SpringerLink (Online, ed. Social Network Data Analytics. Boston, MA: Springer Science+Business Media, LLC, 2011.

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Caviglione, Luca, Mauro Coccoli, and Alessio Merlo. Social network engineering for secure web data and services. Hershey, PA: Information Science Reference, 2013.

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Ting, I.-Hsien. Social network mining, analysis, and research trends: Techniques and applications. Hershey, PA: Information Science Reference, 2012.

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Tsvetovat, Maksim. Social network analysis for startups. Beijing: O'Reilly, 2011.

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Chen, Jiming. Data dissemination and query in mobile social networks. New York: Springer, 2012.

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Mining the social web. Sebastopol, CA: O'Reilly, 2011.

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Luczak, Ed. The Leading Edge Forum presents: Extreme data, rethinking the "I" in IT. El Segunda, Calif: Computer Sciences, 2008.

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Luczak, Ed. The Leading Edge Forum presents: Extreme data, rethinking the "I" in IT. El Segunda, Calif: Computer Sciences, 2008.

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Book chapters on the topic "Online social data"

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Eggensperger, Jim, and Natalie Redcross. "Online and Social Media Measurements." In Data-Driven Public Relations Research, 113–22. 1 Edition. | New York : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9781315196688-9.

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Abdesslem, Fehmi Ben, Iain Parris, and Tristan Henderson. "Reliable Online Social Network Data Collection." In Computational Social Networks, 183–210. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4054-2_8.

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Viégas, Fernanda B., and Martin Wattenberg. "Artistic Data Visualization: Beyond Visual Analytics." In Online Communities and Social Computing, 182–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73257-0_21.

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Ansari, Nazneen, Maahi Talreja, and Vaishali Desai. "Data Mining in Online Social Games." In Advances in Intelligent Systems and Computing, 801–5. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0740-5_95.

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Norcie, Gregory, Emiliano De Cristofaro, and Victoria Bellotti. "Bootstrapping Trust in Online Dating: Social Verification of Online Dating Profiles." In Financial Cryptography and Data Security, 149–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41320-9_10.

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Pham, Canh V., Hieu V. Duong, Bao Q. Bui, and My T. Thai. "Budgeted Competitive Influence Maximization on Online Social Networks." In Computational Data and Social Networks, 13–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04648-4_2.

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Chen, Yunmo, and Xinyue Ye. "Online Community Conflict Decomposition with Pseudo Spatial Permutation." In Computational Data and Social Networks, 246–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34980-6_28.

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Zhang, Cheng, Shang Wu, Honglu Jiang, Yawei Wang, Jiguo Yu, and Xiuzhen Cheng. "Attribute-Enhanced De-anonymization of Online Social Networks." In Computational Data and Social Networks, 256–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34980-6_29.

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Zheng, Xianjun Sam, Ilian Sapundshiev, and Robert Rauschenberger. "WikiTable: A New Tool for Collaborative Authoring and Data Management." In Online Communities and Social Computing, 501–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73257-0_55.

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Sahoo, Somya Ranjan, B. B. Gupta, Chang Choi, Ching-Hsien Hsu, and Kwok Tai Chui. "Behavioral Analysis to Detect Social Spammer in Online Social Networks (OSNs)." In Computational Data and Social Networks, 321–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66046-8_26.

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Conference papers on the topic "Online social data"

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Chang, Ming-Yi, and Chih-Ying Tseng. "Detecting Social Anxiety with Online Social Network Data." In 2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE, 2020. http://dx.doi.org/10.1109/mdm48529.2020.00073.

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Chen, Guangde, Bee-Chung Chen, and Deepak Agarwal. "Social Incentive Optimization in Online Social Networks." In WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3018661.3018700.

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Efstathiades, Hariton, Demetris Antoniades, George Pallis, Marios D. Dikaiakos, Zoltan Szlavik, and Robert-Jan Sips. "Online social network evolution: Revisiting the Twitter graph." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840655.

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Zhou, Xiangmin, Dong Qin, Xiaolu Lu, Lei Chen, and Yanchun Zhang. "Online Social Media Recommendation Over Streams." In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019. http://dx.doi.org/10.1109/icde.2019.00088.

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Xu, Jiejun, and Tsai-Ching Lu. "Toward precise user-topic alignment in online social media." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363821.

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Huang, Chao, Dong Wang, Shenglong Zhu, and Daniel Yue Zhang. "Towards unsupervised home location inference from online social media." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840660.

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Mini, U., and K. Poulose Jacob. "Modeling diffusion pattern in online social media." In 2014 International Conference on Data Science & Engineering (ICDSE). IEEE, 2014. http://dx.doi.org/10.1109/icdse.2014.6974630.

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Shen, Chih-Ya, Liang-Hao Huang, De-Nian Yang, Hong-Han Shuai, Wang-Chien Lee, and Ming-Syan Chen. "On Finding Socially Tenuous Groups for Online Social Networks." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3097983.3097995.

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Muhammad Mahbubur Rahman. "Intellectual knowledge extraction from online social data." In 2012 International Conference on Informatics, Electronics & Vision (ICIEV). IEEE, 2012. http://dx.doi.org/10.1109/iciev.2012.6317392.

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Bachpalle, Shital D., and Manisha Desai. "Data security approach for online social network." In 2014 2nd International Conference on Current Trends in Engineering and Technology (ICCTET). IEEE, 2014. http://dx.doi.org/10.1109/icctet.2014.6966299.

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Reports on the topic "Online social data"

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Landwehr, Peter M. A Collection of Economic and Social Data from Glitch, a Massively Multiplayer Online Game. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada586978.

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Grow, André, Daniela Perrotta, Emanuele Del Fava, Jorge Cimentada, Francesco Rampazzo, Beatriz Sofía Gil-Clavel, Emilio Zagheni, René D. Flores, Ilana Ventura, and Ingmar G. Weber. How reliable is Facebook’s advertising data for use in social science research? Insights from a cross-national online survey. Rostock: Max Planck Institute for Demographic Research, April 2021. http://dx.doi.org/10.4054/mpidr-wp-2021-006.

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Droogan, Julian, Lise Waldek, Brian Ballsun-Stanton, and Jade Hutchinson. Mapping a Social Media Ecosystem: Outlinking on Gab & Twitter Amongst the Australian Far-right Milieu. RESOLVE Network, September 2022. http://dx.doi.org/10.37805/remve2022.6.

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Attention to the internet and the online spaces in which violent extremists interact and spread content has increased over the past decades. More recently, that attention has shifted from understanding how groups like the self-proclaimed Islamic State use the internet to spread propaganda to understanding the broader internet environment and, specifically, far-right violent extremist activities within it. This focus on how far right violent extremist—including far-right racially and ethnically motivated violent extremists (REMVEs) within them—create, use, and exploit the online networks in which they exist to promote their hateful ideology and reach has largely focused on North America and Europe. However, in recent years, examinations of those online dynamics elsewhere, including in Australia, is increasing. Far right movements have been active in Australia for decades. While these movements are not necessarily extremist nor violent, understanding how violent far right extremists and REMVEs interact within or seek to exploit these broader communities is important in further understanding the tactics, reach, and impact of REMVEs in Australia. This is particularly important in the online space access to broader networks of individuals and ideas is increasingly expanding. Adding to a steadily expanding body of knowledge examining online activities and networks of both broader far right as well as violent extremist far right populations in Australia, this paper presents a data-driven examination of the online ecosystems in which identified Australian far-right violent extremists exist and interact,1 as mapped by user generated uniform resource locators (URL), or ‘links’, to internet locations gathered from two online social platforms—Twitter and Gab. This link-based analysis has been used in previous studies of online extremism to map the platforms and content shared in online spaces and provide further detail on the online ecosystems in which extremists interact. Data incorporating the links was automatically collected from Twitter and Gab posts from users existing within the online milieu in which those identified far right extremists were connected. The data was collected over three discrete one-month periods spanning 2019, the year in which an Australian far right violent extremist carried out the Christchurch attack. Networks of links expanding out from the Twitter and Gab accounts were mapped in two ways to explore the extent and nature of the online ecosystems in which these identified far right Australian violent extremists are connected, including: To map the extent and nature of these ecosystems (e.g., the extent to which other online platforms are used and connected to one another), the project mapped where the most highly engaged links connect out to (i.e., website domain names), and To explore the nature of content being spread within those ecosystems, what sorts of content is found at the end of the most highly engaged links. The most highly engaged hashtags from across this time are also presented for additional thematic analysis. The mapping of links illustrated the interconnectedness of a social media ecosystem consisting of multiple platforms that were identified as having different purposes and functions. Importantly, no links to explicitly violent or illegal activity were identified among the top-most highly engaged sites. The paper discusses the implications of the findings in light of this for future policy, practice, and research focused on understanding the online ecosystems in which identified REMVE actors are connected and the types of thematic content shared and additional implications in light of the types of non-violent content shared within them.
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Ibrahimi Jarchlo, Ayla, and Lucy King. Survey of consumer perceptions of alternative, or novel, sources of protein. Food Standards Agency, January 2022. http://dx.doi.org/10.46756/sci.fsa.ncn554.

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This report provides an executive summary of a recent consumer poll conducted on alternative proteins. In December 2021, the FSA commissioned Ipsos MORI to conduct an online survey to understand consumer awareness and perceptions of alternative proteins. The survey was conducted with 1,930 adults aged 16-75 living in England, Wales and Northern Ireland. Data was collected between 9th – 11th December 2021 via Ipsos MORI’s online omnibus. The data was weighted to be representative of the adult population aged 16 – 75 living in England, Wales and Northern Ireland on key demographics: age, gender, region, working status and social grade.
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Lazdane, Gunta, Dace Rezeberga, Ieva Briedite, Inara Kantane, Elizabete Pumpure, Ieva Pitkevica, Darja Mihailova, and Marta Laura Gravina. Sexual and reproductive health survey in the time of COVID-19 – Latvia, 2020. Rīga Stradiņš University, February 2021. http://dx.doi.org/10.25143/fk2/j5kxxd.

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The results of the anonymous online survey of people living in Latvia age 18 and over, using internationally (I-SHARE) and nationally validated questionnaire. Data include following variables: Selection, socio-demographics, social distancing measures, couple and family relationships, sexual behavior, access to condoms and contraceptives, access to reproductive health services, antenatal care, pregnancy and maternal and child health, abortion, sexual and gender-based violence, HIV/STI, mental health, and nutrition. (2021-02-08)
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Faith, Becky, Kevin Hernandez, and James Beecher. Digital Poverty in the UK. Institute of Development Studies, August 2022. http://dx.doi.org/10.19088/ids.2022.057.

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As every aspect of life – from job seeking to health care – moves online, digital connectivity is a daily necessity, not a luxury. Against the backdrop of the UK’s worst cost of living crisis in 40 years, discussions about fuel and food poverty are now joined by a new concern with what has become known as digital poverty – challenges affording the cost of online connectivity and devices. Using data from a survey of low-income households, this Policy Briefing explores the extent of digital poverty in the UK and shows how it can exacerbate other forms of poverty among the most disadvantaged households. It also shows how current fixes, including social tariffs aimed at the poorest in society, are not effectively addressing this critical issue.
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Giles Álvarez, Laura, and Jeetendra Khadan. Mind the Gender Gap: A Picture of the Socioeconomic Trends Surrounding COVID-19 in the Caribbean with a Gender Lens. Inter-American Development Bank, December 2020. http://dx.doi.org/10.18235/0002961.

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This paper provides an insight on the gender impact of the COVID-19 pandemic in the Caribbean. The analysis makes use of the April 2020 online COVID-19 survey that the Inter-American Development conducted in all six Caribbean Country Department member countries. We find that the pandemic is having different effects on men and women. For example, job losses have been more prevalent amongst single-females, whilst business closures have been more prevalent amongst single-males. Quality of life also seems to have worsened more for single-females than for single-males and partners (married or common law partnership) and domestic violence against women has been on the rise. Although the coverage of social assistance programs has increased substantially during the pandemic, we find that more targeting of households with single females could be beneficial, particularly as they show lower levels of financial resilience. Going forward, we recommend further gender targeting in social assistance programs and the collection of gender-disaggregated data that will allow for more thorough investigation of the gender effects of these types of shocks.
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Chopra, Deepta, Kas Sempere, and Meenakshi Krishnan. Assessing Unpaid Care Work: A Participatory Toolkit. Institute of Development Studies, March 2021. http://dx.doi.org/10.19088/ids.2021.016.

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This is a participatory toolkit for understanding unpaid care work and its distribution within local communities and families. Together, these tools provide a way of ascertaining and capturing research participants’ understanding of women’s unpaid care work – giving special attention to the lived experiences of carrying out unpaid care work and receiving care. Please note that these tools were developed and used in a pre-Covid-19 era and that they are designed to be implemented through face-to-face interactions rather than online means. We developed the first iteration of these tools in our ‘Balancing Care Work and Paid Work’ project as part of the Growth of Economic Opportunities for Women (GrOW) programme. The mixed-methods project sought to collect data across four countries – India, Nepal, Tanzania, and Rwanda – with data collected in four sites in each country (16 sites in total). The participatory tools were developed with two main intentions: (1) as a data collection tool to gain a broader understanding of the social norms and perspectives of the wider community in each of the 16 sites; and (2) to be implemented with our local partners as a sensitisation tool for the community regarding women’s unpaid care work burdens. While it is not essential to apply these tools in the order that they are presented, or even all of them, we would suggest that this toolkit be used in its entirety, to gather in-depth knowledge of social norms around the distribution of unpaid care, and the impacts that these have on care providers’ lives and livelihoods.
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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, December 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.

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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.
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Pererva, Victoria V., Olena O. Lavrentieva, Olena I. Lakomova, Olena S. Zavalniuk, and Stanislav T. Tolmachev. The technique of the use of Virtual Learning Environment in the process of organizing the future teachers' terminological work by specialty. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3868.

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This paper studies the concept related to E-learning and the Virtual Learning Environment (VLE) and their role in organizing future teachers’ terminological work by specialty. It is shown the creation and use of the VLE is a promising approach in qualitative restructuring of future specialists’ vocation training, a suitable complement rather than a complete replacement of traditional learning. The concept of VLE has been disclosed; its structure has been presented as a set of components, such as: the Data-based component, the Communication-based, the Management-and-Guiding ones, and the virtual environments. Some VLE’s potential contributions to the organization of terminological work of future biology teachers’ throughout a traditional classroom teaching, an independent work, and during the field practices has been considered. The content of professionally oriented e-courses “Botany with Basis of Geobotany” and “Latin. Botany Terminology” has been revealed; the ways of working with online definer (guide), with UkrBIN National Biodiversity Information Network, with mobile apps for determining the plant species, with digital virtual herbarium, with free software have been shown. The content of students’ activity in virtual biological laboratories and during virtual tours into natural environment has been demonstrated. The explanations about the potential of biological societies in social networks in view of students’ terminology work have been given. According to the results of empirical research, the expediency of using VLEs in the study of professional terminology by future biology teachers has been confirmed.
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