IARank: Ranking users on twitter in near real-time, based on their information amplification potential

Proceedings of the 2012 ASE International Conference on Social Informatics, SocialInformatics 2012

Volumn , Issue , 2012, Pages 70-77

  Cappelletti, Rafael ^a   Sastry, Nishanth ^a  

^a KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

influence; information amplification; real time rank; Twitter

Indexed keywords

ACCURATE MODELING; CUMULATIVE INFLUENCE; INFLUENCE; NEAR-REAL TIME; NON-TRIVIAL; REAL TIME; TWITTER; USER STUDY;

SOCIAL NETWORKING (ONLINE);

EID: 84881063985     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SocialInformatics.2012.82     Document Type: Conference Paper

References (21)

1. Sakaki, T., Okazaki, M., & Matsuo, Y., Earthquake shakes Twitter users: real-time event detection by social sensors, Proceedings of the 19th international conference on World Wide Web, ACM, 851-860, 2010.
2. Kwak, H., Lee, C., Park, H., & Moon, S., What is Twitter, a social network or a news media?, Proceedings of the 19th international conference on World Wide Web, ACM , 591-600.
3. Hermida, A., Twittering the news, Journalism Practice 4, Taylor & Francis, 297-308, 2010
4. Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, K. P., Measuring user influence intwitter: The Million Follower fallacy, 4th International AAAI Conference on Weblogs and Social Media (ICWSM), 10-17, 2010.
5. Katz, E. and Lazarsfeld, P. F., Personal influence: The part played by people in the flow of mass communications, Transaction Pub, 2006.
6. Weng, J., Lim, E. P., Jiang, J., and He, Q., Twitterrank: finding topic-sensitive influential twitterers, Proceedings of the third ACM international conference on Web search and data mining, ACM, 261-270, 2010.
7. Tunkelang, D., A Twitter Analog to PageRank, http://thenoisychannel.com/ 2009/01/13/a- twitter-analog-topagerank/, 2 July 2012, 2009.
8. Yu, A., Hu, C. V., and Kilzer, A., KHYRank: Using Retweets and Mentions to Predict Influential Users, http://cvhu.org/files/khyrank-paper.pdf, 27 August 2012, 2011.
9. Zhang, M., Sun, C., and Liu, W., Identifying Influential Users Of Micro-Blogging Services: A Dynamic Action-Based Network Approach, PACIS 2011 Proceedings. Paper 223, 2011.
10. Page, L., Brin, S., Motwani, R., and Winograd, T., The PageRank citation ranking: Bringing order to the web, Stanford InfoLab, 1999.
11. Romero, D., Galuba, W., Asur, S., and Huberman, B., Influence and passivity in social media, Machine Learning and Knowledge Discovery in Databases, Springer, 18-33, 2011.
12. Meyer, C. D., Matrix analysis and applied linear algebra: solutions manual, Society for Industrial and Applied Mathematics, 2000.
13. Golub, G. H. and Van Loan, C. F., Matrix computations, Johns Hopkins Univ Pr, 1996.
14. Langville, A. N. and Meyer, C. D., Google page rank and beyond, Princeton Univ Pr, 2006.
15. Gayo-Avello, D. and Brenes, D. J., Overcoming Spammers in Twitter-a tale of five algorithms, Spanish Conference on Information Retrieval (CERI), 2010.
16. Song, J., Lee, S., & Kim, J., Spam filtering in twitter using sender-receiver relationship, Recent Advances in Intrusion Detection, Springer, 301-317, 2011.
17. Oxford Dictionaries, http://oxforddictionaries.com/definition/influence? q=influence, 25 June 2012, 2012.
18. Ghosh, R. and Lerman, K., Predicting influential users in online social networks, Arxiv preprint, arXiv:1005.4882, 2010.
19. Yang, J. & Counts, S., Predicting the speed, scale, and range of information diffusion in twitter, Proc. ICWSM, 2010.
20. Krishnamurthy, B., Gill, P., & Arlitt, M. (2008) A few chirps about twitter, Proceedings of the first workshop on Online social networks, ACM, 19-24, 2008.
21. Leavitt, A., Burchard, E., Fisher, D., and Gilbert, S., The influentials: New approaches for analyzing influence on twitter, Web Ecology Project, http://tinyurl.com/lzjlzq, 2009.