Rudra M. Tripathy
ABSTRACT
In this paper we study and evaluate rumor-like methods for combating the spread of rumors on a social network. We model rumor spread as a diffusion process on a network and suggest the use of an "anti-rumor" process similar to the rumor process. We study two natural models by which these anti-rumors may arise. The main metrics we study are the belief time, i.e., the duration for which a person believes the rumor to be true and point of decline, i.e., point after which anti-rumor process dominates the rumor process. We evaluate our methods by simulating rumor spread and anti-rumor spread on a data set derived from the social networking site Twitter and on a synthetic network generated according to the Watts and Strogatz model. We find that the lifetime of a rumor increases if the delay in detecting it increases, and the relationship is at least linear. Further our findings show that coupling the detection and anti-rumor strategy by embedding agents in the network, we call them beacons, is an effective means of fighting the spread of rumor, even if these beacons do not share information.
- C. Castellano, D. Vilone, and A. Vespignani. Incomplete ordering of the voter model on small-world networks. Europhys. Lett., 63(1):153, 2003.Google Scholar
Cross Ref
- P. Donovan. How idle is idle talk? one hundred years of rumor research. Diogenes, 213:59--82, 2007.Google ScholarCross Ref
- D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD '03: Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137--146. ACM, 2003. Google ScholarDigital Library
- Merck-Article. Polio vaccination fears, 2003. Science in Africa: Africa's First On-Line Science Magazine.Google Scholar
- C. M. Mizell and L. M. Sander. A generalized voter model on complex networks. J. Statist. Phys., 136(1):59--71, 2009.Google ScholarCross Ref
- R. Thompson, R. C. Estrada, D. Daugherty, and A. Clintron-Arias. A deterministic approach to the spread of rumors. Technical report, Mathematical and Theoretical Biology, 2003.Google Scholar
- R. Turner. Disasters, Collective Behavior and Social Organization, chapter Rumors as intensified information seeking: Earthquake rumors in China and the United States, pages 244--256. University of Delaware Press, 1994.Google Scholar
- D. J. Watts and S. H. Strogatz. Collective dynamics of small-world' networks. Nature, 393(6684):440--442, June 1998.Google ScholarCross Ref
- D. H. Zanette. Dynamics of rumor propagation on small-world networks. Phys. Rev. E, 65(4), Mar 2002.Google ScholarCross Ref
Index Terms
- A study of rumor control strategies on social networks
Recommendations
Towards combating rumors in social networks: Models and metrics
Dynamic Networks and Knowledge DiscoveryRumor 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 ...
Rumor Gauge: Predicting the Veracity of Rumors on Twitter
Special Issue on KDD 2016 and Regular PapersThe spread of malicious or accidental misinformation in social media, especially in time-sensitive situations, such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed models for automated ...
The diffusion of misinformation on social media
This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temporal pattern, content mutation, and sources of misinformation. We traced the lifecycle of 17 popular political rumors ...
Comments