# On Event Causality Detection in Tweets

arXiv: https://arxiv.org/abs/1901.03526 · HTML: https://mlsanity.com/paper/1901.03526/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021](https://mlsanity.com/paper/2111.01225/) (2021)
- [Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions](https://mlsanity.com/paper/2311.16579/) (2023)
- [Retrieval Augmented Generation based Large Language Models for Causality Mining](https://mlsanity.com/paper/2505.23944/) (2025)

## Abstract (arXiv)

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.