# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 1 Introduction  
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. However, the extensive development of information platforms has created the problem of \"information overload\". Information changes rapidly and people witnessing or involved in events find it difficult to find valuable topics amongst the massive information. Therefore, 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. Topic detection is originated from the Topic Detection and Tracking (TDT) task and gradually became a hot research topic with the increase of online data [1]. Generally, a topic is defined as something non-trivial happening at a specific time or place [2]. Topic detection techniques can be applied in many realistic scenarios, such as reputation monitoring [3], public opinion monitoring [4] and emergency management [5]. In most cases, \"topic\" and \"event\" can be exchanged [6]. So, the two words can be used interchangeably in this paper. Although there are many studies about event detection on single modality data and have achieved excellent performance, these methods cannot deal with multi-modality data because the multi-modality data are more complex than single-modality data. It contains textual, image or even video data types. Figure 1 gives an example of multi-modality data, in which the left picture shows a political event that composed of textual and image data types, and the right picture shows a sport event that composed of textual and video data types. Compared with the single data form and limited information of single-modality data, multi-modality data can represent real-world events from multiple perspectives and thus provide more comprehensive information of the events. Therefore, many researchers began to conduct research on multi-modality data. However, multi-modality event detection is of great challenge, for that the large amount of multi-modality information has the characteristics of cross platform, multiple modalities, large scale and information redundancy. Different modalities such as text and visual have the \"media gap\", which make the representation of different modalities in different dimension and cannot be measured directly. In recent years, many researchers have surveyed event detection. For example, some works surveyed event detection techniques by the textual data on social media data, which is only single-modality data [7; 8; 9; 10]. [PERSON] et al. [11] reviewed the event detection methods based on several different online platforms. [PERSON] et al. [12] surveyed event detection in audio and video. However, they discuss event detection for different modality separately, rather than combining the various modalities. [PERSON] et al. [13] gave various experiments results of event classification based multi-modality data. However, they mainly focused on event classification and event relevance detection on their designed experiments. [PERSON] et al. [14] mainly reviewed the research of topic evolution maps based on cross-media data from the perspective of topic modeling. [PERSON] et al. [15] reviewed the feature learning and event inference techniques. [PERSON] et al. [16] surveyed multi-modal social event detection. However, they only introduced multimodal data representation methods, but failed to combine data representation methods with event detection in some reviewed works. This paper gives a comprehensive analysis of multi-modality event detection and evolution. We start with event representation technologies, in which the single-modality and multi-modality based methods are all surveyed.