Abstract
As online social networks become extremely popular in these days, people communicate and exchange information for various purposes. In this paper, we investigate patterns of information diffusion and behaviors of participating users in Twitter, which would be useful to verify the effectiveness of marketing and publicity campaigns. We characterize Twitter hashtag cascades corresponding to different topics by exploiting distributions of user influence; cascade ratio and tweet ratio. The cascade ratio indicates an ability of users to spread information to their neighborhoods, and the tweet ratio measures how much each user participates in each topic. We examined these two measures on a real Twitter dataset and found three major diffusion patterns over four topics.
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Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an Influencer: Quantifying Influence on Twitter. In: 4th International Conference on Web Search and Data Mining, pp. 65–74. ACM (2011)
Castillo, C., Mendoza, M., Poblete, B.: Information Credibility on Twitter. In: 20th International Conference on World Wide Web, pp. 675–684. ACM (2011)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring User Influence in Twitter: The Million Follower Fallacy. In: 4th International Conference on Weblogs and Social Media, pp. 10–17. AAAI (2010)
Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information Diffusion Through Blogspace. In: 13th International Conference on World Wide Web, pp. 491–501. ACM (2004)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a Social Network or a News Media? In: 19th International Conference on World Wide Web, pp. 591–600. ACM (2010)
Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., Hurst, M.: Patterns of Cascading Behavior in Large Blog Graphs. In: 7th SIAM International Conference on Data Mining, pp. 551–556. SIAM (2007)
Liben-Nowell, D., Kleinberg, J.: Tracing Information Flow on a Global Scale Using Internet Chain-Letter Data. The National Academy of Sciences, 4633–4638 (2008)
Meeder, B., Karrer, B., Sayedi, A., Ravi, R., Borgs, C., Chayes, J.: We Know Who You Followed Last Summer: Inferring Social Link Creation Times in Twitter. In: 20th International Conference on World Wide Web, pp. 517–526. ACM (2011)
Romero, D.M., Meeder, B., Kleinberg, J.: Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter. In: 20th International Conference on World Wide Web, pp. 695–704. ACM (2011)
Sun, E., Rosenn, I., Marlow, C., Lento, T.: Gesundheit! Modeling Contagion through Facebook News Feed. In: 3rd International Conference on Weblogs and Social Media, pp. 146–153. AAAI (2009)
Scellato, S., Mascolo, C., Musolesi, M., Crowcroft, J.: Track Globally, Deliver Locally: Improving Content Delivery Networks by Tracking Geographic Social Cascades. In: 20th International Conference on World Wide Web, pp. 457–466. ACM (2011)
Weng, J., Lim, E.-P., Jiang, J., He, Q.: TwitterRank: Finding Topic-Sensitive Influential Twitterers. In: 3rd International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)
Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J.: Who Says What to Whom on Twitter. In: 20th International Conference on World Wide Web, pp. 705–714. ACM (2011)
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Rattanaritnont, G., Toyoda, M., Kitsuregawa, M. (2012). Characterizing Topic-Specific Hashtag Cascade in Twitter Based on Distributions of User Influence. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_71
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DOI: https://doi.org/10.1007/978-3-642-29253-8_71
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