What's in a hashtag? Content based prediction of the spread of ideas in microblogging communities

WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining

Volumn , Issue , 2012, Pages 643-652

  Tsur, Oren ^a   Rappoport, Ari ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Hashtags; Information diffusion; Microblogging; Social media; Twitter

Indexed keywords

HASHTAGS; INFORMATION DIFFUSION; MICROBLOGGING; SOCIAL MEDIA; TWITTER;

DATA MINING; FORECASTING; INFORMATION RETRIEVAL; SOCIAL NETWORKING (ONLINE); WORLD WIDE WEB;

TOPOLOGY;

EID: 84858057957     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2124295.2124320     Document Type: Conference Paper

References (36)

1. J. Bibby and H. Toutenburg. Prediction and Improved Estimation in Linear Models. John Wiley & Sons, Inc., New York, NY, USA, 1 edition, 1978.
2. L. Bottou. Stochastic learning. In O. Bousquet and U. von Luxburg, editors, Advanced Lectures on Machine Learning, Lecture Notes in Artificial Intelligence, LNAI 3176, pages 146-168. Springer Verlag, Berlin, 2004. (Pubitemid 39741631)
3. M. Cataldi, L. Di Caro, and C. Schifanella. Emerging topic detection on Twitter based on temporal and social terms evaluation. In Proceedings of the Tenth International Workshop on Multimedia Data Mining, pages 1-10. ACM, 2010.
4. M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummadi. Measuring User Influence in Twitter: The Million Follower Fallacy. In In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM), Washington DC, USA, 2010.
5. W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1029-1038. ACM, 2010.
6. E. Cunha, G. Magno, G. Comarela, V. Almeida, M. Gonçalves, and F. Benevenuto. Analyzing the dynamic evolution of hashtags on twitter: a language-based approach. ACL HLT 2011, page 58, 2011.
7. C. Danescu-Niculescu-Mizil, M. Gamon, and S. Dumais. Mark my words!: linguistic style accommodation in social media. In Proceedings of the 20th international conference on World wide web, pages 745-754. ACM, 2011.
8. D. Davidov, O. Tsur, and A. Rappoport. Enhanced sentiment learning using twitter hashtags and smileys. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pages 241-249. Association for Computational Linguistics, 2010.
9. D. Davidov, O. Tsur, and A. Rappoport. Semi-supervised recognition of sarcastic sentences in twitter and amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pages 107-116. Association for Computational Linguistics, 2010.
10. D. Davis and T. Brock. Use of first person pronouns as a function of increased objective self-awareness and performance feedback. Journal of Experimental Social Psychology, 11(4):381-388, 1975.
11. N. R. Draper and H. Smith. Applied Regression Analysis (Wiley Series in Probability and Statistics). Wiley-Interscience, third edition, April 1998.
12. M. Götz, J. Leskovec, M. McGlohon, and C. Faloutsos. Modeling blog dynamics. In International Conference on Weblogs and Social Media, 2009.
13. A. Goyal, F. Bonchi, and L. Lakshmanan. Learning influence probabilities in social networks. In Proceedings of the third ACM international conference on Web search and data mining, pages 241-250. ACM, 2010.
14. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. In Proceedings of the 13th international conference on World Wide Web, WWW '04, pages 491-501, New York, NY, USA, 2004. ACM. (Pubitemid 40752783)
15. L. Guo, E. Tan, S. Chen, X. Zhang, and Y. Zhao. Analyzing patterns of user content generation in online social networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 369-378. ACM, 2009.
16. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter, 11(1):10-18, 2009.
17. J. Kahn, R. Tobin, A. Massey, and J. Anderson. Measuring emotional expression with the Linguistic Inquiry and Word Count. The American journal of psychology, pages 263-286, 2007.
18. D. Kempe, J. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137-146. ACM, 2003.
19. D. Kempe, J. Kleinberg, and É. Tardos. Inuential nodes in a diffusion model for social networks. Automata, Languages and Programming, pages 1127-1138, 2005. (Pubitemid 41436173)
20. H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, pages 591-600. ACM, 2010.
21. J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright. Convergence properties of the nelder-mead simplex algorithm in low dimensions. SIAM Journal of Optimization, 9:112-147, 1996.
22. G. Leshed, J. Hancock, D. Cosley, P. McLeod, and G. Gay. Feedback for guiding reection on teamwork practices. In Proceedings of the 2007 international ACM conference on Supporting group work, pages 217-220. ACM, 2007.
23. J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Trans. Web, 1, May 2007.
24. J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 497-506. Citeseer, 2009.
25. J. A. Nelder and R. Mead. A Simplex Method for Function Minimization. The Computer Journal, 7(4):308-313, January 1965.
26. B. O Connor, R. Balasubramanyan, B. Routledge, and N. Smith. From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the International AAAI Conference on Weblogs and Social Media, pages 122-129, 2010.
27. D. Ramage, S. Dumais, and D. Liebling. Characterizing microblogs with topic models. In International AAAI Conference on Weblogs and Social Media. The AAAI Press, 2010.
28. H. Robbins and S. Monro. A stochastic approximation method. Annals of Mathematical Statistics, 22:400-407, 1951.
29. 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 Proceedings of the 13th international conference on World Wide Web, WWW '11, 2011.
30. S. Rude, E. Gortner, and J. Pennebaker. Language use of depressed and depression-vulnerable college students. Cognition and Emotion, 18(8):1121-1133, 2004. (Pubitemid 40017206)
31. A. Smola and B. Scholkopf. A tutorial on support vector regression. Statistics and computing, 14(3):199-222, 2004. (Pubitemid 39063488)
32. Y. R. Tausczik and J. W. Pennebaker. The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1):24-54, Mar. 2010.
33. M. Wright. Direct search methods: Once scorned, now respectable. In D. Griffitths and G. Watson, editors, Numerical Analysis, pages 191-208. Addison Wesley, Redwood City, 1995.
34. J. Yang and J. Leskovec. Modeling Information Diffusion in Implicit Networks. In 2010 IEEE International Conference on Data Mining, pages 599-608. IEEE, 2010.
35. J. Yang and J. Leskovec. Patterns of temporal variation in online media. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 177-186. ACM, 2011.
36. T. Zaman, R. Herbrich, J. Van Gael, and D. Stern. Predicting information spreading in twitter. In Workshop on Computational Social Science and the Wisdom of Crowds, NIPS, 2010.