Increasing the veracity of event detection on social media networks through user trust modeling

With the success and ubiquity of large scale, social media networks comes the challenge of assessing the veracity of information shared across them that inform individuals about emerging real-world events and trends. We propose a veracity-assessment model for information dissemination on social media networks that combines natural language processing and machine learning algorithms to mine textual content generated by each user. Large scale social media networks (such as Twitter and Facebook) are considered digital communication platforms, in which information can be quickly and easily exchanged, thereby expanding the breadth of knowledge across the globe. In this paper, four case studies spanning multiple geographic regions, threat scenarios and time frames are investigated, in order to demonstrate how real-world events impact the manner in which information/misinformation is communicated and spread through a social media network. Our results show that metadata associated with each user can provide significant insight on the social media network's users' tendency to accurately discuss a topic.