Predicting Civil Unrest by Categorizing Dutch Twitter Events

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


We propose a system that assigns topical labels to automatically detected events in the Twitter stream. The automatic detection and labeling of events in social media streams is challenging due to the large number and variety of messages that are posted. The early detection of future social events, specifically those associated with civil unrest, has a wide applicability in areas such as security, e-governance, and journalism. We used machine learning algorithms and encoded the social media data using a wide range of features. Experiments show a high-precision (but low-recall) performance in the first step. We designed a second step that exploits classification probabilities, boosting the recall of our category of interest, social action events.
Original languageEnglish
Title of host publicationBNAIC 2016
Subtitle of host publicationProceedings of the 28th Annual Benelux Conference on Artificial Intelligence
Publication statusPublished - 2017


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