Many events referred to on Twitter are of a periodic nature, characterized by roughly constant time intervals in between occurrences. Examples are annual music festivals, weekly television programs, and the full moon cycle. We propose a system that can automatically identify periodic events from Twitter in an unsupervised and open-domain fashion. We first extract events from the Twitter stream by associating terms that have a high probability of denoting an event to the exact date of the event. We compare a timelinebased and a calendar-based approach to detecting periodic patterns from the event dates that are connected to these terms. After applying event extraction on over four years of Dutch tweets and scanning the resulting events for periodic patterns, the calendar-based approach yields a precision of 0.76 on the 500 top-ranked periodic events, while the timeline-based approach scores 0.63.
|Number of pages||9|
|Journal||International Conference Recent Advances in Natural Language Processing, RANLP|
|Publication status||Published - 1 Jan 2015|
|Event||10th International Conference on Recent Advances in Natural Language Processing, RANLP 2015 - Hissar, Bulgaria|
Duration: 7 Sep 2015 → 9 Sep 2015