Intelligent Data Analysis

Towards combating rumors in social networks: Models and metrics

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Rumor is a potentially harmful social phenomenon that has been observed in all human societies in all times. Social networking sites provide a platform for the rapid interchange of information and hence, for the rapid dissemination of unsubstantiated claims that are potentially harmful. In this paper, we study different methods for combating rumors in social networks actuated by the realization that authoritarian methods for fighting rumor have largely failed. Our major insight is that in situations where populations do not answer to the same authority, it is the trust that individuals place in their friends that must be leveraged to fight rumor. In other words, rumor is best combated by something which acts like itself, a message which spreads from one individual to another. We call such messages anti-rumors. We study three natural anti-rumor processes to counter the rumor and present mean field equations that characterize the system. Several metrics are proposed to capture the properties of rumor and anti-rumor processes. The metrics are geared to capture temporal evolution as well as global properties of the processes. We evaluate our methods by simulating rumor and anti-rumor processes on a large data set of around 10 nodes derived from the social networking site Twitter and on a synthetic network of the same size generated according to the Barab\'asi-Albert model.


04 Mar 2013


Intelligent Data Analysis