Event detection in Twitter: A machine-learning approach based on term pivoting

Florian Kunneman, Antal Van Den Bosch

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The large number of messages on Twitter posted each day provide rich insights into real-world events and public opinion. However, it is difficult to automatically distinguish tweets referring to such events from everyday chatter, and subsequently to distinguish significant events affecting many people from insignificant events. We apply a term-pivot approach to event detection from the Twitter stream. In order to filter out noisy and mundane events, we train a machine learning classifier on several rich features, and rank the events based on classifier confidence. After training and re-training the classifier using manually annotated data, we obtain an Fβ=1 score of 0.79. However, a baseline that only takes into account the frequency of the tweets that refer to an event yields a better Fβ=1 score of 0.86. We argue that performance is highly related to the definition of what makes a significant event, and that human understanding of this concept is not uniform.

Original languageEnglish
Pages (from-to)65-72
Number of pages8
JournalBelgian/Netherlands Artificial Intelligence Conference
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event26th Benelux Conference on Artificial Intelligence, BNAIC 2014 - Nijmegen, Netherlands
Duration: 6 Nov 20147 Nov 2014

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