PROJECT INTRODUCTION

1. Civil Unrest Events Analysis on Twitter Fang Jin, Shashidhar Sundareisan, Hang Zhang, Yao Zhang CS5526, Virginia Tech Visualization PROJECT INTRODUCTION How to detect potential civil unrest events accurately has been inconceivable before the advent of social media network. However, as geography and social relationships is becoming increasingly precise, the approach of tagging location for each user’s tweets, combined with influence user detection, allowing us to build reliable models to describe interactions among people. With the help of sentiment analysis, it becomes easier to capture the “unrest” emotions from tweets data. Moreover, discovering user’s region of interest may lend help to capture the hideouts of their unrest place. In this project, we combined the four approaches to detect civil unrest from twitter data, which are: Geocoding, sentiment analysis, influence user identification and region of interest discovery. Six Topics Analysis type: 1.Influential user; 2.Sentiment Analysis Sentiment Analysis Geocoding Twitter Rule-based Algorithm Classification-based Algorithm Emoticon Symbol in Twitter Argentina protest event sentiment distribution. Twitter Classification based algorithm. left: using WEKA; Right: accuracy. Y-axis is the probability, each bin corresponds to different scores Influential User Region of Interest Twitter Mention Graph Left: dark mean influential user; Right: Klout Score distribution 15:49-16:49 14:49-15:49 16:49-17:49 17:49-18:49 Region of interest for hours Boston Marathon Bombing