Threat detection in tweets with trigger patterns and contextual cues

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Presented at JISIC 2014 by

Martijn Spitters

Pieter Eendebak

Daniël Worm

Henri Bouma

Many threats in the real world can be related to activities in public sources on the internet. Early detection of threats based on internet information could assist in the prevention of incidents. However, the amount of data in social media, blogs and forums rapidly increases and it is time consuming for security services to monitor all these sources. Therefore, it is important to have a system that automatically ranks messages based on their threat potential and thereby allows security operators to check these messages more efficiently. In this paper, we present a novel method for detecting threatening messages on Twitter based on trigger keywords and contextual cues. The system was tested on multiple large collections of Dutch tweets. Our experimental results show that our system can successfully analyze messages and recognize threatening content.