Event Causality Detection in Tweets by Context Word Extension and Neural Networks

Abstract

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 prescriptive event analytics. Existing approaches include both rule-based and data-driven supervised methods. However, it is challenging to identify event causality accurately using linguistic rules due to the 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 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.