Mining Twitter for an Explanatory Model of Social Influence
نویسندگان
چکیده
The large-scale availability of online communication data offers an opportunity to learn about social influence on the individual level. Starting from an abstract cognitive definition, we iteratively build a predictive model of social influence upon the principle of locality of influence, which implies the decomposition of observed behavior into resistance to influence, and influence received via direct and indirect exposure to others’ behavior. After training the model on a 30,000 user dataset of the social network service Twitter, we find that direct exposure has much less explanatory value than expected, and sources of influence exhibit strong temporal variation. We identify two modes of communication on Twitter, differing in the manifestation of influence.
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