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.
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Notes
- 1.
A live event detection system using the method of [9] is available at http://lamaevents.cls.ru.nl/.
- 2.
TOR requests are an indication of the number of people who choose to hide their identity and location.
- 3.
Since we wanted to provide our system with as much training data as possible, we also extracted relevant tweets that were posted after the event took place. Obviously, when predicting events in the future, this type of data will be unavailable.
- 4.
In addition to Naive Bayes, we experimented with Support Vector Machines and K-nearest neighbors. We will only report on the outcomes of Naive Bayes, which yielded the best performance.
- 5.
- 6.
This was calculated by using the weighted setting in scikit-learn, which is why the F-score is not necessarily between precision and recall.
- 7.
In personal communication, we asked Alan Ritter about this distribution. Unfortunately, he was unable to recover the document with the specific division of categories in the test set.
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van Noord, R., Kunneman, F.A., van den Bosch, A. (2017). Predicting Civil Unrest by Categorizing Dutch Twitter Events. In: Bosse, T., Bredeweg, B. (eds) BNAIC 2016: Artificial Intelligence. BNAIC 2016. Communications in Computer and Information Science, vol 765. Springer, Cham. https://doi.org/10.1007/978-3-319-67468-1_1
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