Predicting civil unrest by categorizing Dutch twitter events

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 t... Mehr ...

Verfasser: van Noord, Rik
Kunneman, Florian A.
van den Bosch, Antal
Dokumenttyp: contributionToPeriodical
Erscheinungsdatum: 2017
Verlag/Hrsg.: Springer Verlag
Schlagwörter: Civil unrest / Event categorization / Event detection
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29027403
Datenquelle: BASE; Originalkatalog
Powered By: BASE
Link(s) : https://hdl.handle.net/11370/2c3ff274-5cd9-408e-a5d2-c819afd1d127

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.