Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing influence in a competitive social network: a follower's perspective
Proceedings of the ninth international conference on Electronic commerce
Prediction of Information Diffusion Probabilities for Independent Cascade Model
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Blocking links to minimize contamination spread in a social network
ACM Transactions on Knowledge Discovery from Data (TKDD)
A note on maximizing the spread of influence in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Almost tight bounds for rumour spreading with conductance
Proceedings of the forty-second ACM symposium on Theory of computing
A study of rumor control strategies on social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Finding spread blockers in dynamic networks
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
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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 10^5 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.