ABSTRACT
This work addresses the problem of detecting topic-based influencers in social media. For that end, we devise a novel behavioral model of authors and readers, where authors try to influence readers by generating ``\emph{attractive}" content, which is both \emph{relevant} and \emph{unique}, and readers can become authors themselves by further citing or referencing content made by other authors. The model is realized by means of a content-based citation graph, where nodes represent authors with their generated content and edges represent reader-to-author citations. To find the top influencers for a given topic, we first profile the content of authors (nodes) and citations (edges) and derive topic-based similarity scores to the topic, which further model the unique and relevant topic interests of users. We then present three different extensions of the Topic-Sensitive PageRank algorithm that exploit the similarity scores to find topic-based influencers. We evaluate our solution on a large real-world dataset that was gathered from Twitter by measuring information diffusion in social networks. We show that, overall, our methods outperform several state-of-the-art methods. This work further serves as an evidence that the topic uniqueness aspect in user interests within social media should be considered for the influencers detection task; this is in comparison to previous works that have solely focused on detecting topic-based influencers using the combination of link structure and topic-relevance.
- N. Agarwal, H. Liu, L. Tang, and P. S. Yu. Identifying the influential bloggers in a community. In WSDM '08: Proceedings of the international conference on Web search and web data mining, pages 207--218, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts. Everyone's an influencer: quantifying influence on twitter. In Proceedings of WSDM, WSDM '11, pages 65--74, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- N. Barbieri, F. Bonchi, and G. Manco. Topic-aware social influence propagation models. In Proceedings of the 2012 IEEE 12th International Conference on Data Mining, ICDM '12, pages 81--90, Washington, DC, USA, 2012. IEEE Computer Society. Google ScholarDigital Library
- B. Bi, Y. Tian, Y. Sismanis, A. Balmin, and J. Cho. Scalable topic-specific influence analysis on microblogs. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM '14, pages 513--522, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
- M. Bianchini, M. Gori, and F. Scarselli. Inside pagerank. ACM Transactions on Internet Technology (TOIT), 5(1):92--128, 2005. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993--1022, 2003. Google ScholarDigital Library
- A. E. Cano, S. Mazumdar, and F. Ciravegna. Social influence analysis in microblogging platforms--a topic-sensitive based approach. Semantic Web, 2011.Google Scholar
- M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummadi. Measuring User Influence in Twitter: The Million Follower Fallacy. In Proceedings of ICWSM.Google Scholar
- D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In WWW '04: Proceedings of the 13th international conference on World Wide Web, pages 491--501, New York, NY, USA, 2004. ACM. Google ScholarDigital Library
- T. H. Haveliwala. Topic-sensitive pagerank: A context-sensitive ranking algorithm for web search. IEEE Transactions on Knowledge and Data Engineering, 15:784--796, 2003. Google ScholarDigital Library
- D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137--146, New York, NY, USA, 2003. ACM. Google ScholarDigital Library
- H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of WWW, WWW '10, pages 591--600, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Trans. Web, 1(1), May 2007. Google ScholarDigital Library
- M. Mathioudakis and N. Koudas. Efficient identification of starters and followers in social media. In EDBT '09: Proceedings of the 12th International Conference on Extending Database Technology, pages 708--719, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. In Stanford Digital Libraries Working Paper, 1998.Google Scholar
- H. Roitman, D. Carmel, Y. Mass, and I. Eiron. Modeling the uniqueness of the user preferences for recommendation systems. In Proceedings of SIGIR, SIGIR '13, pages 777--780, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- X. Shi, K. Chang, V. K. Narayanan, V. Josifovski, and A. J. Smola. A compression framework for generating user profiles. In SIGIR Workshops, 2010.Google Scholar
- M. Shmueli-Scheuer, H. Roitman, D. Carmel, Y. Mass, and D. Konopnicki. Extracting user profiles from large scale data. In Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud, MDAC '10, pages 4:1--4:6, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- A. Silva, S. Guimar\ aes, W. Meira, Jr., and M. Zaki. Profilerank: Finding relevant content and influential users based on information diffusion. In Proceedings of the 7th Workshop on Social Network Mining and Analysis, SNAKDD '13, pages 2:1--2:9, New York, NY, USA, 2013. ACM. Google ScholarDigital Library
- X. Song, Y. Chi, K. Hino, and B. Tseng. Identifying opinion leaders in the blogosphere. In CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pages 971--974, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In Proceedings of SIGKDD, KDD '09, pages 807--816, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- C. Teng, L. Gong, A. Livne, C. Brunetti, and L. A. Adamic. Coevolution of network structure and content. In WebSci, pages 288--297, 2012. Google ScholarDigital Library
- G. Ver Steeg and A. Galstyan. Information transfer in social media. In Proceedings of WWW, WWW '12, pages 509--518, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pages 261--270, New York, NY, USA, 2010. ACM. Google ScholarDigital Library
- C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to information retrieval. ACM Trans. Inf. Syst., 22(2):179--214, 2004. Google ScholarDigital Library
Index Terms
- An author-reader influence model for detecting topic-based influencers in social media
Recommendations
How Social Media Influencers Govern Sentiment Territory
In present research, the authors examined how social media influencers affect the overall sentiment of a topic. To this end, they utilized supervised machine learning approach to develop SentiRobo for measuring the sentiment score of social media ...
Detecting bursts in sentiment-aware topics from social media
Nowadays plenty of user-generated posts, e.g., sina weibos, are published on the social media. The posts contain the publics sentiments (i.e., positive or negative) towards various topics. Bursty sentiment-aware topics from these posts reveal sentiment-...
Examining Information on Social Media: Topic Modelling, Trend Prediction and Community Classification
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information RetrievalIn the past decade, the use of social media networks (e.g. Twitter) increased dramatically becoming the main channels for the mass public to express their opinions, ideas and preferences, especially during an election or a referendum. Both researchers ...
Comments