Abstract
Previous studies on detecting blogosphere influence diffusion had used blog features such as in-degree and sentiment links. The approaches in most of these studies assumed that influence increases with the number of links and largely ignored the possible effect of bloggers’ influence style on the diffusion of influence between linked bloggers where influence could be further described through the engagement style, persuasion style, and the persona of the bloggers. In this paper, we propose an Influence Diffusion Detection Model – Influence Style (IDDM-IS) that includes the use of bloggers’ influence styles to detect influence diffusion through the blogosphere. Our study analyzed 107 bloggers with varying influence styles to detect the influence diffusion path. The results showed performance for IDDM-IS to be better than the in-degree and sentiment-values baseline approaches. In addition, IDDM-IS could provide a fine-grained description of the influence diffusion paths using the bloggers’ influence styles.
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References
Adar, E., Adamic, L.A.: Tracking Information Epidemics in Blogspace. In: Conference on Web Intelligence, pp. 207–214 (2005)
Agarwal, N., Liu, H.: Modeling and Data Mining in Blogosphere. Morgan & Claypool (2009)
Cai, K.K., Bao, S.H., Yang, Z., Tang, J., Ma, R., Zhang, L., Su, Z.: OOLAM: An Opinion Oriented Link Analysis Model for Influence Persona Discovery. In: Web Search and Data Mining, pp. 645–654 (2011)
Cohen, J.A.: Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37–46 (1960)
Costa Jr., P.T., McCrae, R.R.: Normal personality assessment in clinical practice: The NEO Personality Inventory. Psychological Assessment 4, 5–3 (1992)
Goldenberg, J., Libai, B., Muller, E.: Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth. Marketing Letters 12(3), 211–223 (2001)
Granovetter, M.: Threshold models of collective behavior. American Journal of Sociology 83(6), 1420–1443 (1978)
Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: Proceedings of the 13th International Conference on World Wide Web, pp. 491–501 (2004)
Guadagno, R.E., Okdie, B.M., Eno, C.A.: Who blogs? Personality predictors of blogging. Computers in Human Behavior 24(5) (2008)
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting Positive and Negative Links in Online Social Networks. In: World Wide Web, pp. 641–650 (2010)
Lim, S.-H., Kim, S.-W., Kim, S., Park, S.: Construction of a blog network based on information diffusion. In: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 937–941 (2011)
Matsumura, N., Yamamoto, H., Tomozawa, D.: Finding Influencers and Consumer Insights in the Blogosphere. In: International Conference on Weblogs and Social Media AAAI, pp. 76–83 (2008)
Tan, L.K.W., Na, J.-C., Theng, Y.-L., Chang, K.Y.: Blog Site Profiling through Influence Style Detection. In: International Conference on Asian Digital Libraries ACM, pp. 329–332 (2012)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In: Human Language Technology Conference on Empirical Methods in Natural Language Processing ACL, pp. 347–354 (2005)
Yarkoni, T.: Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of Research in Personality (2010)
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Tan, L.KW., Na, JC., Theng, YL. (2013). Influence Diffusion Detection Using Blogger’s Influence Style. In: Urs, S.R., Na, JC., Buchanan, G. (eds) Digital Libraries: Social Media and Community Networks. ICADL 2013. Lecture Notes in Computer Science, vol 8279. Springer, Cham. https://doi.org/10.1007/978-3-319-03599-4_16
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DOI: https://doi.org/10.1007/978-3-319-03599-4_16
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