Rumor Detection based on Social Immune Network

The dissemination of rumors on social media will severely endanger political, economic, and social security, which highlights the importance of rumor detec-tion. Current studies mainly focus on capturing content information or propaga-tion pattern of message cascade, but most of these methods do not describe pre-cisely the potential impact among tweets and tweet's influence in message cas-cade. To tackle the above issue, this paper considers the spread of a rumor on so-cial media as the procedure of immune response in organism, where the users as immune cells, and the retweets as antibodies. A rumor detection model based on Social Immune Network is proposed, named SIN, which is able to utilize the in-stantaneous rate of change in the number of immune cells users and antibodies retweets with certain stance to describe tweet's influence. In this process, inter-actions among different retweets and users with different stances can be explored, thereby investigating the potential impact of each tweet. Extensive experiments conducted based on PHEME dataset show that SIN outperforms State-Of-The-Art method, with 2.8 higher in F1 value of 84.7 , and 2.9 higher in accuracy of 86.2 .