Event Detection in Twitter

Authors

  • Jianshu Weng Service Platform Lab, HP Labs Singapore
  • Bu-Sung Lee Service Platform Lab, HP Labs Singapore and School of Computer Engineering, Nanyang Technological University

Abstract

Twitter, as a form of social media, is fast emerging in recent years. Users are using Twitter to report real-life events. This paper focuses on  detecting those events by analyzing the text stream in Twitter. Although event detection has long been a research topic, the characteristics of Twitter make it a non-trivial task. Tweets reporting such events are usually overwhelmed by high flood of meaningless “babbles”. Moreover, event detection algorithm needs to be scalable given the sheer amount of tweets. This paper attempts to tackle these challenges with EDCoW (Event Detection with Clustering of Wavelet-based Signals). EDCoW builds signals for individual words by applying wavelet analysis on the frequencybased raw signals of the words. It then filters away the trivial words by looking at their corresponding signal autocorrelations. The remaining words are then clustered to form events with a modularity-based graph partitioning technique. Experimental results show promising result of EDCoW.

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Published

2021-08-03

How to Cite

Weng, J., & Lee, B.-S. (2021). Event Detection in Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 401-408. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14102