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The influence of feedback with different opinions on continued user participation in online newsgroups
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-3219-9598
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2013 (English)In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, IEEE Computer Society, 2013, p. 388-395Conference paper, Published paper (Refereed)
Abstract [en]

With the popularity of social media in recent years, it has been a critical topic for social network designer to understand the factors that influence continued user participation in online newsgroups. Our study examines how feedback with different opinions is associated with participants' lifetime in online newsgroups. Firstly, we propose a new method of classifying different opinions among user interaction contents. Generally, we leverage user behavior information in online newsgroups to estimate their opinions and evaluate our classification results based on linguistic features. In addition, we also implement this opinion classification method into our SINCERE system as a real-time service. Based on this opinion classification tool, we use survival analysis to examine how others' feedback with different opinions influence continued participation. In our experiment, we analyze more than 88,770 interactions on the official Occupy LA Facebook page. Our final result shows that not only the feedback with the same opinions as the user, but also the feedback with different opinions can motivate continued user participation in online newsgroup. Furthermore, an interaction of feedback with both the same and different opinions can boost user continued participation to the greatest extent. This finding forms the basis of understanding how to improve online service in social media. Copyright 2013 ACM.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013. p. 388-395
Keywords [en]
Continued user participation, Online newsgroups, Opinion classification, Social influence, Behavioral research, Classification (of information), Information services, Classification methods, Classification results, Classification tool, Continued participations, Influence of feedback, Newsgroups, User participation, Social networking (online)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-10892DOI: 10.1145/2492517.2492555Scopus ID: 2-s2.0-84893317677ISBN: 9781450322409 (print)OAI: oai:DiVA.org:bth-10892DiVA, id: diva2:865143
Conference
2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, 25 August 2013 through 28 August 2013, Niagara Falls, ON
Note

References: O'neill, N., (2010) Google Now Indexes 620 Million Facebook Groups, , http://allfacebook.com/google-now-indexes-620-million-facebook-groups. b10520, Feb; Burke, M., Marlow, C., Lento, T., Feed me: Motivating newcomer contribution in social network sites (2009) Proceedings of the 27th International Conference on Human Factors in Computing Systems, pp. 945-954. , ACM; Joyce, E., Kraut, R., Predicting continued participation in newsgroups (2006) Journal of Computer-Mediated Communication, 11 (3), pp. 723-747; Johnson, S., Impact of Leadership on continued participation in online groups (2008) ProQuest; Cox, D.R., Oakes, D., (1984) Analysis of Survival Data, 21. , Chapman & Hall/CRC; Gouldner, A.W., The norm of reciprocity: A preliminary statement (1960) American Sociological Review, pp. 161-178; Johnson, S., Should i stay or should i go? Continued participation intentions in online communities (2010) Continued Participation Intentions in Online Communities (September 1, 2010). Proceedings of Academy of Management Annual Conference, , Leslie A. Toombs, ed; Wang, Y., Kraut, R., Levine, J., To stay or leave? the relationship of emotional and informational support to commitment in online health support groups (2011) Proceedings of the ACM Conference on Computersupported Cooperative Work; Yang, J., Wei, X., Ackerman, M., Adamic, L., Activity lifespan: An analysis of user survival patterns in online knowledge sharing communities (2010) Proceeding of ICWSM; Stromer-Galley, J., Muhlberger, P., Agreement and disagreement in group deliberation: Effects on deliberation satisfaction, future engagement, and decision legitimacy (2009) Political Communication, 26 (2), pp. 173-192; De Dreu, C.K., West, M.A., Minority dissent and team innovation: The importance of participation in decision making (2001) Journal of Applied Psychology, 86 (6), p. 1191; Eliasoph, N., (1998) Avoiding Politics: How Americans Produce Apathy in Everyday Life, , Cambridge University Press; Mutz, D.C., (2006) Hearing the Other Side: Deliberative Versus Participatory Democracy, , Cambridge University Press; Andreas, J., Rosenthal, S., McKeown, K., Annotating agreement and disagreement in threaded discussion (2012) Proceedings of the 8th International Conference on Language Resources and Computation (LREC), , Istanbul, Turkey, May; Germesin, S., Wilson, T., Agreement detection in multiparty conversation (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 7-14. , ACM; Abbott, R., Walker, M., Anand, P., Fox Tree, J.E., Bowmani, R., King, J., How can you say such things?!?: Recognizing disagreement in informal political argument (2011) Proceedings of the Workshop on Languages in Social Media, pp. 2-11. , Association for Computational Linguistics; Adali, S., Sisenda, F., Magdon-Ismail, M., Actions speak as loud as words: Predicting relationships from social behavior data (2012) Proceedings of the 21st International Conference on World Wide Web, pp. 689-698. , ACM; Hagen, L., Kahng, A.B., New spectral methods for ratio cut partitioning and clustering (1992) Computer-aided Design of Integrated Circuits and Systems, Ieee Transactions on, 11 (9), pp. 1074-1085; Von Luxburg, U., A tutorial on spectral clustering (2007) Statistics and Computing, 17 (4), pp. 395-416; Newman, M.E., Girvan, M., Finding and evaluating community structure in networks (2004) Physical Review e, 69 (2), p. 026113; Tausczik, Y.R., Pennebaker, J.W., The psychological meaning of words: Liwc and computerized text analysis methods (2010) Journal of Language and Social Psychology, 29 (1), pp. 24-54; Walker, M.A., Anand, P., Abbott, R., Tree, J.E.F., Martelly, C., King, J., That's your evidence?: Classifying stance in online political debate (2012) Decision Support Systems; Welch, B.L., The generalization ofstudent's' problem when several different population variances are involved (1947) Biometrika, 34 (1-2), pp. 28-35; Fox, J., Cox proportional-hazards regression for survival data (2002) An R and S-PLUS Companion to Applied Regression, pp. 1-18; Raban, D.R., Moldovan, M., Jones, Q., An empirical study of critical mass and online community survival (2010) Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, pp. 71-80. , ACM; Fisher, D., Smith, M., Welser, H.T., You are who you talk to: Detecting roles in usenet newsgroups (2006) System Sciences, 2006. HICSS'06. Proceedings of the 39th Annual Hawaii International Conference on, 3, pp. 59b-59b. , IEEE

Available from: 2015-10-27 Created: 2015-10-27 Last updated: 2018-01-10Bibliographically approved
In thesis
1. Human Interactions on Online Social Media: Collecting and Analyzing Social Interaction Networks
Open this publication in new window or tab >>Human Interactions on Online Social Media: Collecting and Analyzing Social Interaction Networks
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Online social media, such as Facebook, Twitter, and LinkedIn, provides users with services that enable them to interact both globally and instantly. The nature of social media interactions follows a constantly growing pattern that requires selection mechanisms to find and analyze interesting data. These interactions on social media can then be modeled into interaction networks, which enable network-based and graph-based methods to model and understand users’ behaviors on social media. These methods could also benefit the field of complex networks in terms of finding initial seeds in the information cascade model. This thesis aims to investigate how to efficiently collect user-generated content and interactions from online social media sites. A novel method for data collection that is using an exploratory research, which includes prototyping, is presented, as part of the research results in this thesis.

 

Analysis of social data requires data that covers all the interactions in a given domain, which has shown to be difficult to handle in previous work. An additional contribution from the research conducted is that a novel method of crawling that extracts all social interactions from Facebook is presented. Over the period of the last few years, we have collected 280 million posts from public pages on Facebook using this crawling method. The collected posts include 35 billion likes and 5 billion comments from 700 million users. The data collection is the largest research dataset of social interactions on Facebook, enabling further and more accurate research in the area of social network analysis.

 

With the extracted data, it is possible to illustrate interactions between different users that do not necessarily have to be connected. Methods using the same data to identify and cluster different opinions in online communities have also been developed and evaluated. Furthermore, a proposed method is used and validated for finding appropriate seeds for information cascade analyses, and identification of influential users. Based upon the conducted research, it appears that the data mining approach, association rule learning, can be used successfully in identifying influential users with high accuracy. In addition, the same method can also be used for identifying seeds in an information cascade setting, with no significant difference than other network-based methods. Finally, privacy-related consequences of posting online is an important area for users to consider. Therefore, mitigating privacy risks contributes to a secure environment and methods to protect user privacy are presented.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2018
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 1
Keywords
Social Media, Social Networks, Crawling, Complex Networks, Information Cascade, Seed Selection, Privacy
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15503 (URN)978-91-7295-344-4 (ISBN)
Public defence
2017-01-15, J1650, Karlskrona, 13:00 (English)
Opponent
Supervisors
Available from: 2017-11-23 Created: 2017-11-15 Last updated: 2018-01-13

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