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Publications (10 of 10) Show all publications
Pham, P., Erlandsson, F. & Wu, F. (2017). Social Coordinates: A Scalable Embedding Framework for Online Social Networks. In: Proceedings of the 2017 International Conference on Machine Learning and Soft Computing: . Paper presented at 2017 International Conference on Machine Learning and Soft Computing, ICMLSC, Ho Chi Minh City (pp. 191-196). ACM Digital Library
Open this publication in new window or tab >>Social Coordinates: A Scalable Embedding Framework for Online Social Networks
2017 (English)In: Proceedings of the 2017 International Conference on Machine Learning and Soft Computing, ACM Digital Library, 2017, p. 191-196Conference paper, Published paper (Refereed)
Abstract [en]

We present a scalable framework to embed nodes of a large social network into an Euclidean space such that the proximity between embedded points reflects the similarity between the corresponding graph nodes. Axes of the embedded space are chosen to maximize data variance so that the dimension of the embedded space is a parameter to regulate noise in data. Using recommender system as a benchmark, empirical results show that similarity derived from the embedded coordinates outperforms similarity obtained from the original graph-based measures.

Place, publisher, year, edition, pages
ACM Digital Library, 2017
Keywords
Embedding; Graph kernels; Online social networks
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-14116 (URN)10.1145/3036290.3036298 (DOI)978-1-4503-4828-7 (ISBN)
Conference
2017 International Conference on Machine Learning and Soft Computing, ICMLSC, Ho Chi Minh City
Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2018-01-13Bibliographically approved
Erlandsson, F., Bródka, P., Borg, A. & Johnson, H. (2016). Finding Influential Users in Social Media Using Association Rule Learning. Entropy, 18(5)
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
Keywords
social media, data mining, association rule learning, prediction, social network analysis
National Category
Computer Sciences
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: 2018-01-13Bibliographically approved
Erlandsson, F., Borg, A., Johnson, H. & Bródka, P. (2016). Predicting User Participation in Social Media. In: Wierzbicki A., Brandes U., Schweitzer F., Pedreschi D. (Ed.), Lecture Notes in Computer Science: . Paper presented at 12th International Conference and School on Advances in Network Science, NetSci-X, Wroclaw, Poland (pp. 126-135). Springer, 9564
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, p. 126-135Conference 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 Sciences
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: 2018-01-10Bibliographically approved
Erlandsson, F., Nia, R., Boldt, M., Johnson, H. & Wu, S. F. (2015). Crawling Online Social Networks. In: SECOND EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2015): . Paper presented at Second European Network Intelligence Conference (ENIC) (pp. 9-16). IEEE Computer Society
Open this publication in new window or tab >>Crawling Online Social Networks
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2015 (English)In: SECOND EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2015), IEEE Computer Society, 2015, p. 9-16Conference 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
Keywords
Crawlers;Facebook;Feeds;Informatics;Sampling methods;Silicon compounds;crawling;mining;online social media;online social networks
National Category
Computer Sciences
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: 2018-01-10Bibliographically approved
Erlandsson, F. (2014). On social interaction metrics: social network crawling based on interestingness. (Licentiate dissertation). Karlskrona: Blekinge Institute of Technology
Open this publication in new window or tab >>On social interaction metrics: social network crawling based on interestingness
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

With the high use of online social networks we are entering the era of big data. With limited resources it is important to evaluate and prioritize interesting data. This thesis addresses the following aspects of social network analysis: efficient data collection, social interaction evaluation and user privacy concerns. It is possible to collect data from online social networks via their open APIs. However, a systematic and efficient collection of online social networks data is still challenging. To improve the quality of the data collection process, prioritizing methods are statistically evaluated. Results suggest that the collection time can be reduced by up to 48% by prioritizing the collection of posts. Evaluation of social interactions also require data that covers all the interactions in a given domain. This has previously been hard to do, but the proposed crawler is capable of extracting all social interactions from a given page. With the extracted data it is for instance possible to illustrate indirect 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 been developed. These methods are evaluated with the too Linguistic Inquiry and Word Count. The privacy of the content produced; and the users’ private information provided on social networks is important to protect. Users must be aware of the consequence of posting in online social networks in terms of privacy. Methods to protect user privacy are presented. The proposed crawler in this thesis has, over the period of 20 months, collected over 38 million posts from public pages on Facebook covering: 4 billion likes and 340 million comments from over 280 million users. The performed data collection yielded one of the largest research dataset of social interactions on Facebook today, enabling qualitative research in form of social network analysis.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Institute of Technology, 2014
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 6
National Category
Computer Sciences Media and Communication Technology
Identifiers
urn:nbn:se:bth-00596 (URN)oai:bth.se:forskinfo0BC502A96D245C16C1257D32002E2B6C (Local ID)978-91-7295-287-4 (ISBN)oai:bth.se:forskinfo0BC502A96D245C16C1257D32002E2B6C (Archive number)oai:bth.se:forskinfo0BC502A96D245C16C1257D32002E2B6C (OAI)
Available from: 2014-10-02 Created: 2014-08-12 Last updated: 2018-05-23Bibliographically approved
Nia, R., Erlandsson, F., Johnson, H. & Wu, S. F. (2013). Leveraging Social Interactions to Suggest Friends. In: IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW): . Paper presented at 33rd International Conference on Distributed Computing Systems Workshops (ICDCS 2013 Workshops), Philadelphia, PA, USA (pp. 386-391). IEEE Computer Society
Open this publication in new window or tab >>Leveraging Social Interactions to Suggest Friends
2013 (English)In: IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW), IEEE Computer Society, 2013, p. 386-391Conference paper, Published paper (Refereed)
Abstract [en]

Over the past decade Online Social Networks (OSNs) have made it possible for people to stay in touch with people they already know in real life; although, they have not been able to allow users to grow their personal social network. Existence of many successful dating and friend finder applications online today show the need and importance of such applications. In this paper, we describe an application that leverages social interactions in order to suggest people to users that they may find interesting. We allow users to expand their personal social network using their own interactions with other users on public pages and groups in OSNs. We finally evaluate our application by selecting a random set of users and asking them for their honest opinion.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013
Keywords
social networking (online), OSN, dating applications, friend finder applications, friend suggestion, online social networks, personal social network, social interactions, Communities, Context, Electronic mail, Facebook, Privacy, Silicon compounds, Social Networks
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-10891 (URN)10.1109/ICDCSW.2013.93 (DOI)000332852800067 ()978-1-4799-3247-4 (ISBN)
Conference
33rd International Conference on Distributed Computing Systems Workshops (ICDCS 2013 Workshops), Philadelphia, PA, USA
Available from: 2015-10-27 Created: 2015-10-27 Last updated: 2018-01-17Bibliographically approved
Erlandsson, F., Nia, R., Johnson, H. & Wu, F. (2013). Making social interactions accessible in online social networks. Information Services and Use, 33(2), 113-117
Open this publication in new window or tab >>Making social interactions accessible in online social networks
2013 (English)In: Information Services and Use, ISSN 0167-5265, E-ISSN 1875-8789, Vol. 33, no 2, p. 113-117Article in journal (Refereed) Published
Abstract [en]

Online Social Networks (OSNs) have changed the way people use the internet. Over the past few years these platforms have helped societies to organize riots and revolutions such as the Arab Spring or the Occupying Movements. One key fact in particular is how such events and organizations spread through out the world with social interactions, though, not much research has been focused on how to efficiently access such data and furthermore, make it available to researchers. While everyone in the field of OSN research are using tools to crawl this type of networks our approach differs significantly from the other tools out there since we are getting all interactions related to every single post. In this paper we show means of developing an efficient crawler that is able to capture all social interactions on public communities on OSNs such as Facebook.

Place, publisher, year, edition, pages
IOS Press, 2013
Keywords
Crawling, Online social networks, Social graph
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-6739 (URN)1033/ISU-130702.32 (Scopus ID)oai:bth.se:forskinfoF0C321B783E84AF2C1257CBA00261310 (Local ID)oai:bth.se:forskinfoF0C321B783E84AF2C1257CBA00261310 (Archive number)oai:bth.se:forskinfoF0C321B783E84AF2C1257CBA00261310 (OAI)
Note

Open Access article

Available from: 2014-04-14 Created: 2014-04-14 Last updated: 2018-01-11Bibliographically approved
Wang, T., Wang, K. C., Erlandsson, F., Wu, S. F. & Faris, R. W. (2013). The influence of feedback with different opinions on continued user participation in online newsgroups. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013: . Paper presented at 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 (pp. 388-395). IEEE Computer Society
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, 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
Keywords
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: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: 2018-01-10Bibliographically approved
Erlandsson, F., Boldt, M. & Johnson, H. (2012). Privacy threats related to user profiling in online social networks. In: : . Paper presented at 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. IEEE
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 Sciences
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: 2018-01-11Bibliographically approved
Nia, R., Erlandsson, F., Bhattacharyya, P., Rahman, M. R., Johnson, H. & Wu, S. F. (2012). SIN: A Platform to Make Interactions in Social Networks Accessible. In: Proceedings of the 2012 ASE International Conference on Social Information: . Paper presented at International Conference on Social Informatics (SocialInformatics, Washington D.C. (pp. 205-214). IEEE conference proceedings
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, p. 205-214Conference 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 Sciences
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: 2018-01-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3219-9598

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