A Model for Identifying Misinformation in Online Social Networks

Online Social Networks (OSNs) have become increasingly popular means of information sharing among users. The spread of news regarding emergency events is common in OSNs and so is the spread of misinformation related to the event. We define as misinformation any false or inaccurate information that is spread either intentionally or unintentionally. In this paper we study the problem of misinformation identification in OSNs, and we focus in particular on the Twitter social network. Based on user and tweets characteristics, we build a misinformation detection model that identifies suspicious behavioral patterns and exploits supervised learning techniques to detect misinformation. Our extensive experimental results on 80294 unique tweets and 59660 users illustrate that our approach effectively identifies misinformation during emergencies. Furthermore, our model manages to timely identify misinformation, a feature that can be used to limit the spread of the misinformation.