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User-driven geolocated event detection in social media

Anes Bendimerad 1 Marc Plantevit 1 Céline Robardet 1 Sihem Amer-Yahia 2
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 SLIDE [2016-2019] - ScaLable Information Discovery and Exploitation [Grenoble] [2016-2019]
LIG [2016-2019] - Laboratoire d'Informatique de Grenoble [2016-2019]
Abstract : Event detection is one of the most important research topics in social media analysis. Despite this interest, few researchers have addressed the problem of identifying geolocated events in an unsupervised way, and none includes user interests during the process. In this paper, we tackle the problem of local event detection from social media data. We present a method to automatically identify events by evaluating the burstiness of hashtags in a geographical area and a time interval, and at the same time integrating user feedback. We devise two algorithms to discover user-driven events. The first one relies on an exact enumeration process, while the other directly samples the space of events. In our empirical study, we provide evidence that geolocated events cannot be detected by non location-aware methods. We also show that our methods (i) outperform by a factor of two to several orders of magnitude state-of-the-art methods designed to discover geolocated events, (ii) are more robust to noise, (iii) and produce high quality events with respect to user interests.
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Submitted on : Wednesday, October 2, 2019 - 10:49:47 AM
Last modification on : Wednesday, August 5, 2020 - 3:01:57 AM

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Anes Bendimerad, Marc Plantevit, Céline Robardet, Sihem Amer-Yahia. User-driven geolocated event detection in social media. IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, In press, ⟨10.1109/TKDE.2019.2931340⟩. ⟨hal-02272082⟩

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