Veracity assessment by single and multi-source identification algorithms during the crisis

Abstract

Social networks have become a popular communication tool for information sharing. Twitter offers access to data and provides a significant opportunity to analyze data. During pandemics, Twitter becomes a big source for the dispersal of unverified information. In social media, it is difficult to find the sources of rumors. To tackle this problem the authors have developed a hybrid rumor centrality algorithm for rumor source detection in social networks. The authors propose an S-RSI algorithm for identifying a single rumor centre and an M-RSI algorithm for identifying the propagations of multiple rumor centres in the thread of conversation. The proposed rumor centrality algorithm efficiently predicts the rumor disseminating possibilities in a conversation tree with the aid of graph theoretical approach. The authors have evaluated the performance of the algorithms on the PHEME dataset containing seven real-time event conversational trees based on the tweet messages. The results show that the proposed is best suitable in finding the rumor source centre with a high probability in social media during a crisis.