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Human Interactions on Online Social Media: Collecting and Analyzing Social Interaction Networks
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-3219-9598
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
Keyword [en]
Social Media, Social Networks, Crawling, Complex Networks, Information Cascade, Seed Selection, Privacy
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-15503ISBN: 978-91-7295-344-4 (print)OAI: oai:DiVA.org:bth-15503DiVA: diva2:1157269
Public defence
2017-01-15, J1650, Karlskrona, 13:00 (English)
Opponent
Supervisors
Available from: 2017-11-23 Created: 2017-11-15 Last updated: 2017-12-06Bibliographically approved
List of papers
1. Privacy threats related to user profiling in online social networks
Open this publication in new window or tab >>Privacy threats related to user profiling in online social networks
2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The popularity of Online Social Networks (OSNs) has increased the visibility of users profiles and interactions performed between users. In this paper we structure different privacy threats related to OSNs and describe six different types of privacy threats. One of these threats, named public information harvesting, is previously not documented so we therefore present it in further detail by also presenting the results from a proof-of-concept implementation of that threat. The basis of the attack is gathering of user interactions from various open groups on Facebook which then is transformed into a social interaction graph. Since the data gathered from the OSN originates from open groups it could be executed by any third-party connected to the Internet independently of the users' privacy settings. In addition to presenting the different privacy threats we also we propose a range of different protection techniques.

Place, publisher, year, edition, pages
IEEE, 2012
National Category
Computer Science
Identifiers
urn:nbn:se:bth-6928 (URN)10.1109/SocialCom-PASSAT.2012.16 (DOI)oai:bth.se:forskinfo0DC19F2B64199DA5C1257B9B0049DADE (Local ID)978-076954848-7 (ISBN)oai:bth.se:forskinfo0DC19F2B64199DA5C1257B9B0049DADE (Archive number)oai:bth.se:forskinfo0DC19F2B64199DA5C1257B9B0049DADE (OAI)
Conference
ASE/IEEE International Conference on Social Computing, SocialCom 2012 and the 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2012, Amsterdam
Note

Article number6406334

Available from: 2013-07-01 Created: 2013-07-01 Last updated: 2017-11-15Bibliographically approved
2. SIN: A Platform to Make Interactions in Social Networks Accessible
Open this publication in new window or tab >>SIN: A Platform to Make Interactions in Social Networks Accessible
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2012 (English)In: Proceedings of the 2012 ASE International Conference on Social Information, IEEE conference proceedings, 2012, 205-214 p.Conference paper, Published paper (Refereed)
Abstract [en]

Online Social Networks (OSNs) are popular platforms for interaction, communication and collaboration between friends. In this paper we develop and present a new platform to make interactions in OSNs accessible. Most of today's social networks, including Facebook, Twitter, and Google+ provide support for third party applications to use their social network graph and content. Such applications are strongly dependent on the set of software tools and libraries provided by the OSNs for their own development and growth. For example, third party companies like CNN provide recommendation materials based on user interactions and user's relationship graph. One of the limitations with this graph (or APIs) is the segregation from the shared content. We believe, and present in this paper, that the content shared and the actions taken on the content, creates a Social Interaction Network (SIN). As such, we extend Facebook's current API in order to allow applications to retrieve a weighted graph instead of Facebooks unweighted graph. Finally, we evaluate the proposed platform based on completeness and speed of the crawled results from selected community pages. We also give a few example uses of our API on how it can be used by third party applications.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2012
National Category
Computer Science
Identifiers
urn:nbn:se:bth-10889 (URN)10.1109/SocialInformatics.2012.29 (DOI)978-1-4799-0234-7 (ISBN)
Conference
International Conference on Social Informatics (SocialInformatics, Washington D.C.
Available from: 2015-10-27 Created: 2015-10-27 Last updated: 2017-11-15Bibliographically approved
3. The influence of feedback with different opinions on continued user participation in online newsgroups
Open this publication in new window or tab >>The influence of feedback with different opinions on continued user participation in online newsgroups
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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, 388-395 p.Conference 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
Keyword
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 Science
Identifiers
urn:nbn:se:bth-10892 (URN)10.1145/2492517.2492555 (DOI)2-s2.0-84893317677 (Scopus ID)9781450322409 (ISBN)
External cooperation:
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: 2017-11-15Bibliographically approved
4. Mining User Deliberation and Bias in Online Newsgroups: A Dynamic View
Open this publication in new window or tab >>Mining User Deliberation and Bias in Online Newsgroups: A Dynamic View
2015 (English)In: Proceedings of the 2015 ACM on Conference on Online Social Networks, ACM , 2015, 209-219 p.Conference paper, Published paper (Refereed)
Abstract [en]

Social media is changing many different aspects of our lives. By participating in online discussions, people exchange opinions on various topics, shape their stances, and gradually form their own characteristics. In this paper, we propose a framework for identifying online user characteristics and understanding the formation of user deliberation and bias in online newsgroups. In the first section of the paper, we propose a dynamic user-like graph model for recognizing user deliberation and bias automatically in online newsgroups. In addition, we evaluate our identification results with linguistic features and implement this model in our SINCERE system as a real-time service. In the second section, after applying this model to two large online newsgroups, we analyze the influence of early discussion context on the formation of user characteristics. Our conclusion is that user deliberation and bias are a product of situations, not simply dispositions: confronting disagreement in unfamiliar circumstances promotes more consideration of different opinions, while recurring conflict in familiar circumstances evokes close-minded behavior and bias. Based on this observation, we also build a supervised learning model to predict user deliberation and bias at an early online life-stage. Our results show that having only the first three months of users' interaction data generates an F1 accuracy level of around 70% in predicting user deliberation and bias in online newsgroups. This work has practical significance for people who design and maintain online newsgroups. It yields new insights into opinion diffusion and has wide potential applications in politics, education, and online social media.

Place, publisher, year, edition, pages
ACM, 2015
Series
COSN ’15
Keyword
dynamic graph, opinion diffusion, social media
National Category
Computer Science
Identifiers
urn:nbn:se:bth-15507 (URN)10.1145/2817946.2817951 (DOI)978-1-4503-3951-3 (ISBN)
Conference
ACM Conference on Online Social Networks (COSN´15), Stanford University
Available from: 2017-11-15 Created: 2017-11-15 Last updated: 2017-11-16Bibliographically approved
5. Crawling Online Social Networks
Open this publication in new window or tab >>Crawling Online Social Networks
Show others...
2015 (English)In: SECOND EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2015), IEEE Computer Society, 2015, 9-16 p.Conference paper, Published paper (Refereed)
Abstract [en]

Researchers put in tremendous amount of time and effort in order to crawl the information from online social networks. With the variety and the vast amount of information shared on online social networks today, different crawlers have been designed to capture several types of information. We have developed a novel crawler called SINCE. This crawler differs significantly from other existing crawlers in terms of efficiency and crawling depth. We are getting all interactions related to every single post. In addition, are we able to understand interaction dynamics, enabling support for making informed decisions on what content to re-crawl in order to get the most recent snapshot of interactions. Finally we evaluate our crawler against other existing crawlers in terms of completeness and efficiency. Over the last years we have crawled public communities on Facebook, resulting in over 500 million unique Facebook users, 50 million posts, 500 million comments and over 6 billion likes.

Place, publisher, year, edition, pages
IEEE Computer Society, 2015
Keyword
Crawlers;Facebook;Feeds;Informatics;Sampling methods;Silicon compounds;crawling;mining;online social media;online social networks
National Category
Computer Science
Identifiers
urn:nbn:se:bth-10993 (URN)10.1109/ENIC.2015.10 (DOI)000375081700002 ()
Conference
Second European Network Intelligence Conference (ENIC)
Available from: 2016-02-02 Created: 2015-11-20 Last updated: 2017-11-15Bibliographically approved
6. Predicting User Participation in Social Media
Open this publication in new window or tab >>Predicting User Participation in Social Media
2016 (English)In: Lecture Notes in Computer Science / [ed] Wierzbicki A., Brandes U., Schweitzer F., Pedreschi D., Springer, 2016, Vol. 9564, 126-135 p.Conference paper, Published paper (Other academic)
Abstract [en]

Abstract Online social networking services like Facebook provides a popular way for users to participate in different communication groups and discuss relevant topics with each other. While users tend to have an impact on each other, it is important to better understand and ...

Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9564
National Category
Computer Science
Identifiers
urn:nbn:se:bth-11533 (URN)10.1007/978-3-319-28361-6_10 (DOI)978-3-319-28360-9 (ISBN)
Conference
12th International Conference and School on Advances in Network Science, NetSci-X, Wroclaw, Poland
Available from: 2016-02-02 Created: 2016-02-02 Last updated: 2017-11-15Bibliographically approved
7. Finding Influential Users in Social Media Using Association Rule Learning
Open this publication in new window or tab >>Finding Influential Users in Social Media Using Association Rule Learning
2016 (English)In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 18, no 5Article in journal (Refereed) Published
Abstract [en]

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI AG, 2016
Keyword
social media, data mining, association rule learning, prediction, social network analysis
National Category
Computer Science
Identifiers
urn:nbn:se:bth-13575 (URN)10.3390/e18050164 (DOI)000377262900009 ()
Note

open access

Available from: 2016-12-12 Created: 2016-12-12 Last updated: 2017-11-29Bibliographically approved
8. Seed selection for information cascade in multilayer networks
Open this publication in new window or tab >>Seed selection for information cascade in multilayer networks
2017 (English)In: Complex Networks & Their Applications VI: Proceedings of the 6th International Workshop on Complex Networks and their Applications (COMPLEX NETWORKS 2017), Springer-Verlag New York, 2017Chapter in book (Refereed)
Abstract [en]

Information spreading is an interesting field in the domain of online social media. In this work, we are investigating how well different seed selection strategies affect the spreading processes simulated using independent cascade model on eighteen multilayer social networks. Fifteen networks are built based on the user interaction data extracted from Facebook public pages and tree of them are multilayer networks downloaded from public repository (two of them being Twitter networks). The results indicate that various state of the art seed selection strategies for single-layer networks like K-Shell or VoteRank do not perform so well on multilayer networks and are outperformed by Degree Centrality.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2017
Series
Studies in Computational Intelligence, ISSN 1860-949X
National Category
Computer Science
Identifiers
urn:nbn:se:bth-15326 (URN)
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2017-11-15Bibliographically approved
9. Do We Really Need To Catch Them All?: A New User-Guided Social Media Crawling Method
Open this publication in new window or tab >>Do We Really Need To Catch Them All?: A New User-Guided Social Media Crawling Method
2017 (English)In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300Article in journal (Refereed) Submitted
Abstract [en]

With the growing use of popular social media services like Facebook and Twitter it is hard to collect all content from the networks without access to the core infrastructure or paying for it. Thus, if all content cannot be collected one must consider which data are of most importance.In this work we present a novel User-Guided Social Media Crawling method (USMC) that is able to collect data from social media, utilizing the wisdom of the crowd to decide the order in which user generated content should be collected, to cover as many user interactions as possible. USMC is validated by crawling 160 Facebook public pages, containing 368 million users and 1.3 billion interactions, and it is compared with two other crawling methods. The results show that it is possible to cover approximately 75% of the interactions on a Facebook page by sampling just 20% of its posts, and at the same time reduce the crawling time by 53%.What is more, the social network constructed from the 20% sample has more than 75% of the users and edges compared to the social network created from all posts, and has very similar degree distribution.

Place, publisher, year, edition, pages
MDPI, 2017
Keyword
social media, social networks, sampling, crawling
National Category
Computer Science
Identifiers
urn:nbn:se:bth-15508 (URN)
Available from: 2017-11-15 Created: 2017-11-15 Last updated: 2017-11-29Bibliographically approved

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