Recent trends in event detection from Twitter using multimodal data

Abstract

A large amount of social media data hosted on platforms like Twitter, Instagram, Facebook, etc. are event-based and hold a substantial amount of real-world data. Event-based information can appear on any social media site in the form of news items, images, videos, audio clips, status updates, etc. The task of event detection refers to identifying data relevant to an event and the classification of this relevant data to different event types. Traditional social media event detection techniques focused mainly on a single modality as the data shared were mostly homogenous. However, the current social media data is multimodal and includes text, images, audio, and video clips, and geolocations. Multimodal event detection techniques are essential for handling such heterogeneous data. Among all the social media sites Twitter is the most popular as users share event-related short messages and photos in real-time generating several thousands of tweets very frequently. In this paper, we focus on providing a comprehensive survey of event detection from social media, especially from the widely used platform, Twitter. The survey focuses mainly on research done on event detection using the two main modalities single and multimodality. At the end of the paper, we discuss the relevance of multimodal event detection from social media data which currently spans multiple dimensions.