FINDING NEWS-TOPIC ORIENTED INFLUENTIAL TWITTER USERS BASED ON TOPIC RELATED HASHTAG COMMUNITY DETECTION
Keywords:
Social Network Analysis, Twitter, hashtag, PageRank, characteristic co-occurrence wordAbstract
Recently, more and more users would like to collect and provide information about news topics in Twitter, which is one of the most popular microblogging services. Virtual communities defined by hashtags in Twitter are created for exchanging information about the news topic. Finding influential Twitter users in these communities related to a news topic would help us understand why some opinions are popular, and get valuable and reliable information for the news topic. In this paper, we propose a new approach to detect news-topic-related user communities defined by hashtags based on characteristic co-occurrence word detection. We also propose RetweetRank and MentionRank to find two types of influential Twitter users from these news-topic-related communities based on user’s retweet and mention activities. Experimental results show that our characteristic co-occurrence word detection methods could detect words which are highly relevant to the news topic. RetweetRank could find influential Twitter users whose tweets about the news topic are valuable and more likely to interest others. MentionRank could find influential Twitter users who have high authority on the news topic. Our methods also outperform other related methods in evaluations.
Downloads
References
Anger, I. and Kittl, C., Measuring Influence on Twitter. In Proc. of the 11th Int. Conf. on Knowledge Management and Knowledge Technologies, pp.31:1-31:4, ACM (Graz, Austria. 2011).
Bigonha, C., Cardoso, T. N. C., Moro, M. M., Almeida, V. A. F. and Goncalves, M. A., Detecting Evangelists and Detractors on Twitter. In Proc. of the Brazilian Symposium on Multimedia and the Web, pp. 107-114 (Belo Horizonte, Brazil, 2010).
Brin, S. and Page, L., The Anatomy of A Large-scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems Vol. 30, Issue 1-7, pp. 107–117, Elsevier Science Publishers B. V. (1998).
Cano, A. E., Mazumdar, S., and Ciravegna, F., Social Influence Analysis in Microblogging Platforms - A Topic-sensitive based Approach. Special Issue on the Semantics of Microposts. Semantic Web Journal. pp. 1-5, IOS Press (2011).
Celebrating #Twitter7. http://blog.twitter.com/2013/03/celebrating-twitter7.html
Cha, M., Haddadi, H., Benevenuto, F. and Gummadi, K. P., Measuring User Influence in Twitter: The Million Follower Fallacy. In Proc. of the 4th Int. AAAI Conf. on Weblogs and Social Media, AAAI Press (Washington DC, USA, 2010).
Efron, M., Hashtag Retrieval in a Microblogging Environment. In Proc. of the 33rd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 787-788, ACM (Geneva, Switzerland, 2010).
Finkel, J.R., Grenager, T. and Manning, C., Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. In Proc. of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 363-370, ACL (Ann Arbor, Michigan, 2005).
Hajian, B. and White, T., Modelling Influence in a Social Network: Metrics and Evaluation. In IEEE 3rd Int. Conf. on Social Computing, pp. 497-500, IEEE (Boston, MA, USA, 2011).
Huang, J., Thornton, K. M. and Efthimiadis, E. N., Conversational Tagging in Twitter. In Proc. of the 21st ACM Conf. on Hypertext and Hypermedia, pp. 173-178, ACM (Toronto, Canada, 2010).
Jarvelin K. and Kekalainen, J., Cumulated Gain-based Evaluation of IR Techniques. ACM Transactions on Information Systems, Vol.20, Issue 4, pp. 422–446, ACM (2002).
Jones, K. S., A Statistical Interpretation of Term Specificity and its Application in Retrieval. Journal of Documentation, Vol. 28, pp. 11-21. (1972).
Kleinberg, J. Authoritative sources in a Hyperlinked Environment. Journal of the ACM, Vol. 46, Issue 5, pp. 604-632, ACM (1999).
Kwak, H., Lee, C., Park, H. and Moon, S., What is Twitter, a Social Network or a News Media? In Proc. of the 19th Int. Conf. on WWW, pp. 591-600, ACM (Raleigh, North Carolina, USA, 2010).
Kywe, S. M., Hoang, T-A., Lim E-P. and Zhu, F., On Recommending Hashtags in Twitter Networks. In Proc. of the 4th Int. Conf. on Social Information, pp. 337-350, Springer-Verlag (Lausanne, Switzerland, 2012).
Leavitt, A., Burchard, E., Fisher, D. and Gilbert, S. (2009). The Influentials: New Approaches for Analyzing Influence on Twitter. Web Ecology Project. http://www.webecologyproject.org/wp-content/uploads/2009/09/influence-report-final.pdf
Lehmann, J., Gonçalves, B., Ramasco, J. J. and Cattuto, C., Dynamical Classes of Collective Attention in Twitter. In Proc. of the 21st Int. Conf. on WWW, pp. 251-260, ACM (Lyon, France, 2012).
Manning, C. D. and Schutze, H., Foundations of Statistical Natural Language Processing. MIT Press (1999).
Mazzia, A. and Juett, J. (2011), Suggesting Hashtags on Twitter. EECS 545 Project, http://www-personal.umich.edu/~amazzia/pubs/545-final.pdf
Microblogging. http://en.wikipedia.org/wiki/Microblogging
Noro, T., Ru, F., Xiao, F. and Tokuda, T., Twitter User Rank Using Keyword Search. In the 22nd European-Japanese Conf. on Information Modelling and Knowledge Bases, pp. 31-48, IOS Press (Prague, 2012).
Pak A. and Paroubek P., Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In Proc. of the 7th Conf. on Int. Language Resources and Evaluation. pp. 1320-1326, ELRA (Valletta, Malta, 2010).
Romero, D. M., Galuba, W., Asur, S. and Huberman, B. A., Influence and Passivity in Social Media. In ECML/PKDD, pp. 18-33, Springer-Verlag (Athens, Greece, 2011).
Salton, G., Wong, A. and Yang, C.S., A Vector Space Model for Automatic Indexing. Communications of the ACM, Vol. 18, Issue 11, pp. 613-620, ACM (1975).
Schmid, H., Probabilistic Part-of-Speech Tagging Using Decision Trees. In Proc. of Int. Conf. on New Methods in Language Processing, pp. 44-49 (Manchester, UK, 1994).
Singhal, A., Buckley, C. and Mitra, M., Pivoted Document Length Normalization. In Proc. of the 19th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 21–29, ACM (Zurich, Switzerland, 1996).
Weng, J., Lim, E. P., He, Q. and Leung, C. W.-K., What Do People Want in Microblogs? Measuring Interestingness of Hashtags in Twitter. In Proc. of the 2010 IEEE Int. Conf. on Data Mining, pp.1121-1126, IEEE (2010).
Weng, J., Lim, E. P., Jiang, J. and He, Q., TwitterRank: Finding Topic-sensitive Influential Twitterers. In Proc. of the 3rd Int. Conf. on Web Search and Data Mining, pp. 261-270, ACM (New York, USA, 2010).
What Facebook and Twitter Mean for News. http://stateofthemedia.org/files/2012/03/Facebook-and-Twitter-Topline.pdf
Xiao, F., Noro, T. and Tokuda, T., Detection of Characteristic Co-occurrence Words from News Articles on the Web. In the 21st European-Japanese Conf. on Information Modelling and Knowledge Bases, Vol.237, pp.187-203, IOS Press (Tallinn, Estonia, 2011).
Xiao, F., Noro, T. and Tokuda, T., News-Topic Oriented Hashtag Recommendation in Twitter Based on Characteristic Co-occurrence Word Detection. In Proc. of the 12th Int. Conf. on Web Engineering, pp.16-30, Springer-Verlag (Berlin, Germany. 2012).
Yang, L., Sun, T., Zhang, M., and Mei, Q., We Know What @You #Tag: Does the Dual Role Affect Hashtag Adoption? In Proc. of the 21st Int. Conf. on WWW, pp. 261-270, ACM (Lyon, France, 2012).
Ye, S. and Wu, S. F., Measuring Message Propagation and Social Influence on Twitter.com. In Proc. of the 2nd Int. Conf. on Social Informatics, pp. 216-231, Springer-Verlag (Laxenburg, Austria, 2010).
Zangerle, E., Gassler, W. and Specht, G., Recommending #-Tags in Twitter, In Proc. of the Workshop on Semantic Adaptive Social Web. CEUR Workshop Proceedings, pp. 67-78 (2011).
