The social media genome: Modeling individual topic-specific behavior in social media

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013

Volumn , Issue , 2013, Pages 236-242

  Bogdanov, Petko ^a   Busch, Michael ^a   Moehlis, Jeff ^a   Singh, Ambuj K ^a   Szymanski, Boleslaw K ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SOCIAL NETWORKING (ONLINE);

BACKBONE STRUCTURES; INFLUENCE PREDICTIONS; INFORMATION PROPAGATION; REAL NETWORKS; SOCIAL MEDIA; STRUCTURAL FEATURE; USER BEHAVIORS; USER'S INTEREST;

BEHAVIORAL RESEARCH;

EID: 84893224943     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2492517.2492621     Document Type: Conference Paper

References (21)

1. O. Tsur and A. Rappoport, "What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities," in WSDM, 2012, pp. 643-652.
2. D. Kempe, J. Kleinberg, and E. Tardos, "Maximizing the spread of influence through a social network," in SIGKDD, 2003, pp. 137-146.
3. M. Kimura, K. Saito, R. Nakano, and H. Motoda, "Extracting influential nodes on a social network for information diffusion," DMKD, 2010.
4. H. Kwak, C. Lee, H. Park, and S. Moon, "What is Twitter, a social network or a news media?" in WWW, 2010, pp. 591-600.
5. D. M. Romero, B. Meeder, and J. Kleinberg, "Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter," in WWW, 2011.
6. N. Friedkin, A Structural Theory of Social Influence. Cambridge University Press, 2006, vol. 13.
7. M. E. J. Newman, "The structure and function of complex networks," SIAM Review, vol. 45, pp. 167-256, 2003.
8. P. Dodds, K. Harris, I. Kloumann, C. Bliss, and C. Danforth, "Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter," PLoS One, vol. 6, no. 12, p. e26752, 2011.
9. J. Yang and J. Leskovec, "Patterns of temporal variation in online media," in WSDM, 2011, pp. 177-186.
10. R. Bandari, S. Asur, and B. A. Huberman, "The pulse of news in social media: Forecasting popularity," in ICWSM, 2012.
11. J. Weng, E.-P. Lim, J. Jiang, and Q. He, "Twitterrank: finding topicsensitive influential Twitterers," in WSDM, 2010, pp. 261-270.
12. C. X. Lin, Q. Mei, Y. Jiang, J. Han, and S. Qi, "Inferring the diffusion and evolution of topics in social communities," SNMA, 2011.
13. B. Suh, L. Hong, P. Pirolli, and E. Chi, "Want to be retweeted? large scale analytics on factors impacting retweet in Twitter network," in SocialCom, 2010, pp. 177-184.
14. D. Ramage, S. Dumais, and D. Liebling, "Characterizing microblogs with topic models," in ICWSM 2010, vol. 5, no. 4, 2010, pp. 130-137.
15. M. Gomez Rodriguez, J. Leskovec, and B. Schölkopf, "Structure and dynamics of information pathways in online media," in WSDM, 2013.
16. E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, "Everyone's an influencer: quantifying influence on Twitter," in WSDM, 2011.
17. A. Pal and S. Counts, "Identifying topical authorities in microblogs," in WSDM, 2011, pp. 45-54.
18. P. Bogdanov, M. Busch, J. Moehlis, A. K. Singh, and B. K. Szymanski, "The social media genome: Modeling individual topic-specific behavior in social media, (supplemental material),http://cs.ucsb.edu/%7edbl/papers/ genomeappendix.pdf," 2013.
19. R. Ribiero, "25 small-business Twitter hashtags to follow," http://www.biztechmagazine.com/article/2012/06/25-small-businesstwitter- hashtags-follow, 2012.
20. A. K. McCallum, "MALLET: A machine learning for language toolkit," http://mallet.cs.umass.edu, 2002.
21. S. Brin and L. Page, "The anatomy of a large-scale hypertextual web search engine," Computer networks and ISDN systems, vol. 30, no. 1, pp. 107-117, 1998. 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 242