Abstract
The rapid growth of online data has made it very convenient for people to obtain information. However, it also leads to the problem of “information overload”. Therefore, how to detect hot events from the massive amount of information has always been a problem. With the development of multimedia platforms, event detection has gradually developed from traditional single modality detection to multi-modality detection and is receiving increasing attention. The goal of multi-modality event detection is to discover events from a huge amount of online data with different data structures, such as texts, images and videos. These data represent real-world events from different perspectives so that they can provide more information about an event. In addition, event evolution is also a meaningful research direction; it models how events change dynamically over time and has great significance for event analysis. This paper comprehensively reviews the existing research on event detection and evolution. We first give a series of necessary definitions of event detection and evolution. Next, we discuss the techniques of data representation for event detection, including textual, visual, and multi-modality content. Finally, we review event evolution under multi-modality data. Furthermore, we review several public datasets and compare their results. At the end of this paper, we provide a conclusion and discuss future work.
Highlights
With the development of various online platforms, such as news media platforms, social media websites (e.g., Twitter) and image/video sharing platforms (e.g., Flickr), users can conveniently get information from the internet or share texts, images or videos anytime and anywhere by using their smartphones
In the first year (2011), [120] achieved the best performance in challenge 1, mainly because it classifies the photos to cities at first and partitioned the photos into buckets that contains the photos of the same day or same city
In challenge 2, [121] achieved the best performance, mainly because the approach matches the photos to event descriptions retrieved from online event directories
Summary
With the development of various online platforms, such as news media platforms (e.g., google news), social media websites (e.g., Twitter) and image/video sharing platforms (e.g., Flickr), users can conveniently get information from the internet or share texts, images or videos anytime and anywhere by using their smartphones. Information changes rapidly and people witnessing or involved in events find it difficult to find valuable topics amongst the massive information. It is a problem for people to find meaningful topics from the massive online data. One solution to this problem is called Topic Detection (TD), which focuses on mining real-world occurrences in unprecedentedly vast online data. Contains 73,645 images of five cities: Amsterdam, Barcelona, London, Paris and Rome. Contains 167,332 images of five cities: Barcelona, Madrid, Cologne, Hamburg and Hannover. Contains 427,370 images and 1327 videos with XML timestamps, geographic information, tags, titles, descriptions, etc. The metadata of the two datasets are available.
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