Predicting time-to-event from Twitter messages

Hannah Tops, Antal Van Den Bosch, Florian Kunneman

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


We describe a system that estimates when an event is going to happen from a stream of microtexts on Twitter referring to that event. Using a Twitter archive and 60 known football events, we train machine learning classifiers to map unseen tweets onto discrete time segments. The time period before the event is automatically segmented; the accuracy with which tweets can be classified into these segments determines the error (RMSE) of the time-to-event prediction. In a cross-validation experiment we observe that support vector machines with χ2 feature selection attain the lowest prediction error of 52.3 hours off. In a comparison with human subjects, humans produce a larger error, but recognize more tweets as posted before the event; the machine-learning approach more often misclassifies a 'before' tweet as posted during or after the event.

Original languageEnglish
Title of host publicationBNAIC 2013
Subtitle of host publicationProceedings of the 25th Benelux Conference on Artificial Intelligence
Number of pages8
Publication statusPublished - 1 Jan 2013
Externally publishedYes
Event25th Benelux Conference on Artificial Intelligence, BNAIC 2013 - Delft, Netherlands
Duration: 7 Nov 20138 Nov 2013

Publication series

NameBelgian/Netherlands Artificial Intelligence Conference
ISSN (Print)1568-7805


Conference25th Benelux Conference on Artificial Intelligence, BNAIC 2013


Dive into the research topics of 'Predicting time-to-event from Twitter messages'. Together they form a unique fingerprint.

Cite this