A deep learning model for mining and detecting causally related events in tweets

Humayun Kayesh*, Md Saiful Islam, Junhu Wang, A. S.M. Kayes, Paul A. Watters

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Nowadays, public gatherings and social events are an integral part of a modern city life. To run such events seamlessly, it requires real time mining and monitoring of causally related events so that the management can make informed decisions and take appropriate actions. The automatic detection of event causality from short text such as tweets could be useful for event management in this context. However, detecting event causality from tweets is a challenging task. Tweets are short, unstructured, and often written in highly informal language which lacks enough contextual information to detect causality. The existing approaches apply different techniques including hand-crafted linguistic rules and machine learning models. However, none of the approaches tackle the issue related to the lack of contextual information. In this paper, we detect event causality in tweets by applying a context word extension technique and a deep causal event detection model. The context word extension technique is driven by background knowledge extracted from one million news articles. Our model achieves 79.35% recall and 67.28% f1-score, which are 17.39% and 2.33% improvements to the state-of-the-art approach.

Original languageEnglish
Number of pages15
JournalConcurrency Computation
DOIs
Publication statusE-pub ahead of print - 22 Jul 2020
Externally publishedYes

Keywords

  • context word extension
  • deep learning
  • event causality
  • feature enhancement

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