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Modeling Indirect Influence on Twitter

Xin Shuai, Ying Ding, Jerome Busemeyer, Shanshan Chen, Yuyin Sun, Jie Tang

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)8(4)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781466614888|DOI: 10.4018/jswis.2012100102

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MLA

Shuai, Xin, et al. "Modeling Indirect Influence on Twitter." IJSWIS vol.8, no.4 2012: pp.20-36. http://doi.org/10.4018/jswis.2012100102

APA

Shuai, X., Ding, Y., Busemeyer, J., Chen, S., Sun, Y., & Tang, J. (2012). Modeling Indirect Influence on Twitter. International Journal on Semantic Web and Information Systems (IJSWIS), 8(4), 20-36. http://doi.org/10.4018/jswis.2012100102

Chicago

Shuai, Xin, et al. "Modeling Indirect Influence on Twitter," International Journal on Semantic Web and Information Systems (IJSWIS) 8, no.4: 20-36. http://doi.org/10.4018/jswis.2012100102

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Abstract

Social influence in social networks has been extensively researched. Most studies have focused on direct influence, while another interesting question can be raised as whether indirect influence exists between two users who’re not directly connected in the network and what affects such influence. In addition, the theory of complex contagion tells us that more spreaders will enhance the indirect influence between two users. The authors’ observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability in two Twitter social networks, shows that complex contagion is validated globally but is violated locally. In other words, the retweeting probability increases non-monotonically with some local drops. A quantum cognition based probabilistic model is proposed to account for these local drops.

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Modeling Indirect Influence on Twitter

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