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Analyzing Behavior of the Influentials Across Social Media

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Behavior Computing


The popularity of social media as an information source, in the recent years has spawned several interesting applications, and consequently challenges to using it effectively. Identifying and targeting influential individuals on sites is a crucial way to maximize the returns of advertising and marketing efforts. Recently, this problem has been well studied in the context of blogs, microblogs, and other forms of social media sites. Understanding how these users behave on a social media site and even across social media sites will lead to more effective strategies. In this book chapter, we present existing techniques to identify influential individuals in a social media site. We present a user identification strategy, which can help us to identify influential individuals across sites. Using a combination of these approaches we present a study of the characteristics and behavior of influential individuals across sites. We evaluate our approaches on several of the popular social media sites. Among other interesting findings, we discover that influential individuals on one site are more likely to be influential on other sites as well. We also find that influential users are more likely to connect to other influential individuals.

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This research was funded in part by the National Science Foundation’s Social-Computational Systems (SoCS) program and Human Centered Computing (HCC) program within the Directorate for Computer and Information Science and Engineering’s Division of Information and Intelligent Systems (award numbers: IIS - 1110868 and IIS - 1110649) and by the U.S. Office of Naval Research (award number: N000141010091). We gratefully acknowledge this support.

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Agarwal, N., Kumar, S., Gao, H., Zafarani, R., Liu, H. (2012). Analyzing Behavior of the Influentials Across Social Media. In: Cao, L., Yu, P. (eds) Behavior Computing. Springer, London.

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