@inproceedings{2c3ff2745cd9408ea5d2c819afd1d127,
title = "Predicting civil unrest by categorizing Dutch twitter events",
abstract = "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.",
keywords = "Civil unrest, Event categorization, Event detection",
author = "\{van Noord\}, Rik and Kunneman, \{Florian A.\} and \{van den Bosch\}, Antal",
year = "2017",
month = sep,
day = "15",
doi = "10.1007/978-3-319-67468-1\_1",
language = "English",
isbn = "978-3-319-67467-4",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "3--16",
editor = "Bert Bredeweg and Tibor Bosse",
booktitle = "BNAIC 2016 Benelux Conference on Artificial Intelligence",
edition = "28",
note = "28th Benelux Conference on Artificial Intelligence, BNAIC 2016 ; Conference date: 10-11-2016 Through 11-11-2016",
}