A survey on multi-modal social event detection

Due to the prevalence of social media sites, users are allowed to conveniently share their ideas and activities anytime and anywhere. Therefore, these sites hold substantial real-world event related data. Different from traditional social event detection methods which mainly focus on single-media, multimodal social event detection aims at discovering events in vast heterogeneous data such as texts, images and video clips. These data denote real-world events from multiple dimensions simultaneously so that they can provide comprehensive and complementary understanding of social event. In recent years, multi-modal social event detection has attracted intensive attentions. This paper concentrates on conducting a comprehensive survey of extant works. Two current attempts in this field are firstly reviewed: event feature learning and event inference. Particularly, event feature learning is a prerequisite because of its ability on translating social media data into computer-friendly numerical form. Event inference aims at deciding whether a sample belongs to a social event. Then, several public datasets in the community are introduced and the comparison results are also provided. At the end of this paper, a general discussion of the insights is delivered to promote the development of multi-modal social event detection. (C) 2020 Elsevier B.V. All rights reserved.