Are Recent Terrorism Trends Reflected in Social Media?

Abstract

Social media plays an important role in shaping the beliefs and sentiments of an audience regarding an event. A comparison between public data sets that have holistic features and social media data set that include more user features would give insight into the spread of misinformation and aspects of events that are reflected in user behavior. In this research, we compare the trends identified in the public data set - Global Terrorism Database (GTD) with the trends reflected through the social media data obtained using the Twitter API. The unsupervised learning algorithm Self-Organizing Map (SOM) is used to identify the features and trends summarized by the clusters. The results show discrepancies in the features and related trends of terrorism events in the GTD data set and obtained Twitter data set to suggest some media bias and public perception on terrorism.