Event Detection in Twitter: A Content and Time-Based Analysis

TL;DR

This paper presents a time-series analysis approach for detecting events on Twitter by analyzing the frequency of top n-grams over time, demonstrated through a case study of the 2015 Mizzou protests.

Contribution

It introduces a method combining content and temporal analysis of n-grams for event detection on social media, validated with real-world Twitter data.

Findings

High-frequency n-grams correlate with event occurrences

The model accurately detected the Mizzou protests

Temporal analysis captures evolving event dynamics

Abstract

The detection of events from online social networks is a recent, evolving field that attracts researchers from across a spectrum of disciplines and domains. Here we report a time-series analysis for predicting events. In particular, we evaluated the frequency distribution of top n-grams of terms over time, focusing on two indicators: high-frequency n-grams over both short and long periods of time. Both indicators can refer to certain aspects of events as they evolve. To evaluate the models accuracy in detecting events, we built and used a Twitter dataset of the most popular hashtags that surrounded the well-documented protests that occurred at the University of Missouri (Mizzou) in late 2015.