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Detection of Trending Topic Communities: Bridging Content Creators and Distributors

Authors:

Lorena Recalde,

David F. Nettleton,

Ricardo Baeza-Yates,

Ludovico BorattoAuthors Info & Claims

HT '17: Proceedings of the 28th ACM Conference on Hypertext and Social Media

Pages 205 - 213

https://doi.org/10.1145/3078714.3078735

Published: 04 July 2017 Publication History

Abstract

The rise of a trending topic on Twitter or Facebook leads to the temporal emergence of a set of users currently interested in that topic. Given the temporary nature of the links between these users, being able to dynamically identify communities of users related to this trending topic would allow for a rapid spread of information. Indeed, individual users inside a community might receive recommendations of content generated by the other users, or the community as a whole could receive group recommendations, with new content related to that trending topic. In this paper, we tackle this challenge, by identifying coherent topic-dependent user groups, linking those who generate the content (creators) and those who spread this content, e.g., by retweeting/reposting it (distributors). This is a novel problem on group-to-group interactions in the context of recommender systems. Analysis on real-world Twitter data compare our proposal with a baseline approach that considers the retweeting activity, and validate it with standard metrics. Results show the effectiveness of our approach to identify communities interested in a topic where each includes content creators and content distributors, facilitating users' interactions and the spread of new information.

References

[1]

S. Berkovsky and J. Freyne. 2015. Personalised Network Activity Feeds: Finding Needles in the Haystacks. In Mining, Modeling, and Recommending "Things" in Social Media, Martin Atzmueller, Alvin Chin, Christoph Scholz, and Christoph Trattner (Eds.). Lecture Notes in Computer Science, Vol. 8940. Springer International Publishing, 21--34.

Digital Library

[2]

V. D. Blondel, J. L. Guillaume, R. Lambiotte, and E. Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008, 10 (2008), P10008.

[3]

P. Bonhard and M. A. Sasse. 2006. "Knowing Me, Knowing You" -- Using Profiles and Social Networking to Improve Recommender Systems. BT Technology Journal 24, 3 (July 2006), 84--98.

Digital Library

[4]

M. Cha, H. Haddadi, F. Benevenuto, and K.P. Gummadi. 2010. Measuring user influence in Twitter: The million follower fallacy. In 4th International AAAI Conference on Weblogs and Social Media (ICWSM).

[5]

J. Chen, R. Nairn, L. Nelson, M. Bernstein, and E. Chi. 2010. Short and Tweet: Experiments on Recommending Content from Information Streams. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10). ACM, New York, NY, USA, 1185--1194.

Digital Library

[6]

K. Chen, T. Chen, G. Zheng, O. Jin, E. Yao, and Y. Yu. 2012. Collaborative Personalized Tweet Recommendation. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '12). ACM, New York, NY, USA, 661--670.

Digital Library

[7]

Y. Chen, L. Cheng, and Ch. Chuang. 2008. A group recommendation system with consideration of interactions among group members. Expert Systems with Applications 34, 3 (2008), 2082--2090.

Digital Library

[8]

U. M. Dholakia, R. P. Bagozzi, and L. K. Pearo. 2004. A social influence model of consumer participation in network- and small-group-based virtual communities. International Journal of Research in Marketing 21, 3 (2004), 241--263.

[9]

I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, and S. Ofek-Koifman. 2009. Personalized Recommendation of Social Software Items Based on Social Relations. In Proceedings of the Third ACM Conference on Recommender Systems (RecSys '09). ACM, New York, NY, USA, 53--60.

Digital Library

[10]

J. Hannon, M. Bennett, and B. Smyth. 2010. Recommending Twitter Users to Follow Using Content and Collaborative Filtering Approaches. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys '10). ACM, New York, NY, USA, 199--206.

Digital Library

[11]

E. Katz and P. F. Lazarsfeld. 1955. Personal Influence, the Part Played by People in the Flow of Mass Communications. The Free Press, Chicago, USA.

[12]

S. M. Kywe, T. A. Hoang, E. P. Lim, and F. Zhu. 2012. On Recommending Hashtags in Twitter Networks. In Proceedings of the 4th International Conference on Social Informatics (SocInfo'12). Springer-Verlag, Berlin, Heidelberg, 337--350.

Digital Library

[13]

P. Lops, M. de Gemmis, and G. Semeraro. 2011. Content-based Recommender Systems: State of the Art and Trends. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B. Kantor (Eds.). Springer US, 73--105.

[14]

H. Ma, D. Zhou, Ch. Liu, M. Lyu, and I. King. 2011. Recommender Systems with Social Regularization. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM '11). New York, USA, 287--296.

Digital Library

[15]

L. Quijano-Sanchez, J. Recio-Garcia, B. Diaz-Agudo, and G. Jimenez-Diaz. 2013. Social Factors in Group Recommender Systems. ACM Transactions on Intelligent Systems and Technology 4, 1, Article 8 (Feb. 2013), 30 pages.

Digital Library

[16]

J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen. 2007. Collaborative Fil- tering Recommender Systems. In The Adaptive Web, Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl (Eds.). Lecture Notes in Computer Science, Vol. 4321. Springer Berlin Heidelberg, 291--324.

Digital Library

[17]

R. R. Sinha and K. Swearingen. 2001. Comparing Recommendations Made by Online Systems and Friends. In DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries.

[18]

D. J. Watts and P. S. Dodds. 2007. Influentials, Networks, and Public Opinion Formation. Journal of Consumer Research 34, 4 (2007), 441--458.

[19]

J. Weng, E. P. Lim, J. Jiang, and Q. He. 2010. TwitterRank: Finding Topic-sensitive Influential Twitterers. In Proceedings of the Third ACM International Conference on Web Search and Data Mining (WSDM '10). ACM, New York, NY, USA, 261--270.

Digital Library

[20]

M. Ye, X. Liu, and W. Lee. 2012. Exploring Social Influence for Recommendation: A Generative Model Approach. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '12). ACM, New York, NY, USA, 671--680.

Digital Library

[21]

X. Zhou, Y. Xu, Y. Li, A. Josang, and C. Cox. 2012. The state-of-the-art in personalized recommender systems for social networking. Artificial Intelligence Review 37, 2 (2012), 119--132.

Digital Library

[22]

Arkaitz Zubiaga, Damiano Spina, Raquel Martínez-Unanue, and Víctor Fresno. 2015. Real-time classification of Twitter trends. JASIST 66, 3 (2015), 462--473.

Cited By

Abou-Of M(2020)A Fuzzy, Incremental and Semantic Trending Topic Detection in Social Feeds2020 11th International Conference on Information and Communication Systems (ICICS)10.1109/ICICS49469.2020.239492(118-124)Online publication date: Apr-2020
https://doi.org/10.1109/ICICS49469.2020.239492
Myat Mon Oo May Thu Lwin (2019)Analysis of Online Social Network after an EventComputer and Information Science10.1007/978-3-030-25213-7_11(163-177)Online publication date: 7-Aug-2019
https://doi.org/10.1007/978-3-030-25213-7_11
Abou-Of MSaad HDarwish S(2019)Smart and Incremental Model to Build Clustered Trending Topics of Web Documents10.1007/978-3-030-14118-9_87(888-897)Online publication date: 17-Mar-2019
https://doi.org/10.1007/978-3-030-14118-9_87

Index Terms

Detection of Trending Topic Communities: Bridging Content Creators and Distributors
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
      2. Recommender systems
  2. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Social networking sites

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Information

Published In

cover image ACM Conferences

HT '17: Proceedings of the 28th ACM Conference on Hypertext and Social Media

July 2017

336 pages

ISBN:9781450347082

DOI:10.1145/3078714

General Chairs:
Peter Dolog
Aalborg University, Denmark
,
Peter Vojtas
Charles University, Prague, Czech Republic
,
Program Chairs:
Francesco Bonchi
ISI Foundation, Turin, Italy
,
Denis Helic
Graz University of Technology, Austria

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 July 2017

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HT'17

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SIGWEB

HT'17: 28th Conference on Hypertext and Social Media

July 4 - 7, 2017

Prague, Czech Republic

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HT '17 Paper Acceptance Rate 19 of 69 submissions, 28%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Abou-Of M(2020)A Fuzzy, Incremental and Semantic Trending Topic Detection in Social Feeds2020 11th International Conference on Information and Communication Systems (ICICS)10.1109/ICICS49469.2020.239492(118-124)Online publication date: Apr-2020
https://doi.org/10.1109/ICICS49469.2020.239492
Myat Mon Oo May Thu Lwin (2019)Analysis of Online Social Network after an EventComputer and Information Science10.1007/978-3-030-25213-7_11(163-177)Online publication date: 7-Aug-2019
https://doi.org/10.1007/978-3-030-25213-7_11
Abou-Of MSaad HDarwish S(2019)Smart and Incremental Model to Build Clustered Trending Topics of Web Documents10.1007/978-3-030-14118-9_87(888-897)Online publication date: 17-Mar-2019
https://doi.org/10.1007/978-3-030-14118-9_87

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