We present a method for the identification of future event start dates from Twitter streams. Taking hashtags or event name expressions as query terms, the method gathers a certain number of tweets about an event and uses clues in these tweets to estimate at what date the event will start. Clues include temporal expressions with knowledge-based and automatically generated estimations, and other predictive words. The estimation is performed either with a machine-learning classifier or by taking a majority vote over the temporal expressions found in the set of tweets. Results show that temporal expressions are indeed strong predictors. The majority-based and machine-learning approaches attain equal performances when trained and tested on a single event type, soccer matches, with an average estimation error of 0:05 days; but when tested on a range of different events, the majority-voting approach shows to be more robust than machine learning for this task, yielding high performance on all events. Still, per-event differences hint at a context in which machine learning might be beneficial.
|Number of pages||14|
|Journal||Computational Linguistics in the Netherlands Journal|
|Publication status||Published - 1 Dec 2014|
|Event||24th Meeting of Computational Linguistics in the Netherlands, CLIN 2014 - Leiden, Netherlands|
Duration: 17 Jan 2014 → 17 Jan 2014