On Event Causality Detection in Tweets

TL;DR

This paper introduces a neural network approach enhanced with background knowledge for detecting causality in tweets, addressing challenges posed by unstructured social media text.

Contribution

It proposes a novel event context word extension technique and applies it within a neural network model for improved causality detection in tweets.

Findings

Our approach outperforms existing methods in accuracy.

The background knowledge extension significantly improves detection performance.

Extensive experiments validate the effectiveness of the proposed method.

Abstract

Nowadays, Twitter has become a great source of user-generated information about events. Very often people report causal relationships between events in their tweets. Automatic detection of causality information in these events might play an important role in predictive event analytics. Existing approaches include both rule-based and data-driven supervised methods. However, it is challenging to correctly identify event causality using only linguistic rules due to the highly unstructured nature and grammatical incorrectness of social media short text such as tweets. Also, it is difficult to develop a data-driven supervised method for event causality detection in tweets due to insufficient contextual information. This paper proposes a novel event context word extension technique based on background knowledge. To demonstrate the effectiveness of our proposed event context word extension technique, we develop a feed-forward neural network based approach to detect event causality from tweets. Extensive experiments demonstrate the superiority of our approach.