Geospatial Event Detection by Grouping Emotion Contagion in Social Media

Twitter has a significant user base, with reportedly over 300 million active user accounts. Twitter, a micro blog service, limits the length of each tweet, keeping them short and concise. The contents of tweets include news, trending topics, emotions, and opinions. This makes Twitter a (popular) source of data for social science, marketing, psychology and news. Twitter users tend to use emojis, slang, and acronyms in order to fit more content within the character limit. The use of emojis in tweets complicates efforts in text mining and emotion analysis, as such emojis can also be used to express sarcasm when used in different contexts. In this paper, we use Twitter API to mine tweets that were geotagged by users and apply text analytics to the tweets. We also develop a system to detect events using the geospatial emotion vector in the area we are monitoring. Combining graph theory, machine learning semantics, and statistics with the geospatial emotion vectors, we track trending topics during times of extreme emotion. Our system correctly detected ten out of 11 events that we used in our study. Our findings suggest that Robert Parks theory on Expressive Groups and Gustave Le Bons Theory on Social Contagion hold true in the Twittersphere.