@article{Kunneman_H¨urriyetoğlu_Oostdijk_van den Bosch_2014, title={Timely identification of event start dates from Twitter}, volume={4}, url={https://clinjournal.org/clinj/article/view/39}, abstractNote={<p>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.</p>}, journal={Computational Linguistics in the Netherlands Journal}, author={Kunneman, Florian and H¨urriyetoğlu, Ali and Oostdijk, Nelleke and van den Bosch, Antal}, year={2014}, month={Dec.}, pages={39–52} }