  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.
