Abstract
In this paper, we propose a method for event detection on social media, which aims at clustering media items into groups of events based on their textural information as well as available metadata. Our approach is based on distance-dependent Chinese Restaurant Process (ddCRP), a clustering approach resembling Dirichlet process algorithm. Furthermore, we scrutinize the effectiveness of a series of pre-processing steps in improving the detection performance. We experimentally evaluated our method using the Social Event Detection (SED) dataset of MediaEval 2013 benchmarking workshop, which pertains to the discovery of social events and their grouping in event-specific clusters. The obtained results indicate that the proposed method attains very good performance rates compared to existing approaches.
Highlights
In recent years, there has been a great research interest in techniques for event detection on data retrieved from Social Networks, focusing mostly on Twitter platform
We can see that our method dependent Chinese Restaurant Process (ddCRP), which is highlighted, outperforms the other three approaches for both measures
We present a method for social media event detection
Summary
There has been a great research interest in techniques for event detection on data retrieved from Social Networks, focusing mostly on Twitter platform. Atefeh et al [2] conducted a study based on three major categories: (i) the type of event being detected, distinguishing the event into specified and unspecified; (ii) the detection task (new event detection and retrospective event detection); and (iii) the event detection method (supervised, unsupervised, and hybrid). Another classification approach of the different detection methods is presented in the survey [3] that focuses on the common traits the methods share (i.e., using probabilistic topic modeling, identifying interesting properties in a tweet’s keywords/terms and using incremental clustering).
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