# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 6 Multi-Modality Event Evolution  
The multi-modality events evolution analysis trains a model based on the event multi-modality data. Event evolution analysis aims to find the correlation among topics at the adjacent time, which is an evolution of the relationship of these topics with the change of time. Event evolution analysis can help the user to understand the development or trend of an event. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Reference** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{Feature fusion based methods} & [79] & Utilize various features & Lack of social network information \\ \cline{2-4}  & [80] & Constrained clustering algorithm is used to achieved high accuracy & Imbalanced data and parameter setting is not optimal \\ \cline{2-4}  & [81] & Automatic concept mining and boosted concept learning & Application is limited \\ \cline{2-4}  & [82] & Unsupervised and without predefined threshold & Lack of more types of semantic features \\ \cline{2-4}  & [83] & Need no manual annotation and can adapt concepts to news domains & - \\ \cline{2-4}  & [84] & Utilized the information contained in the related exemplars & - \\ \hline \multirow{6}{*}{Matrix factorization based methods} & [85] & Robust to data incompleteness & - \\ \cline{2-4}  & [86] & Can discover the shared structure between the datasets & - \\ \cline{2-4}  & [90] & semi-supervised co-clustering with side information & Parameters setting is not automatic \\ \cline{2-4}  & [91] & Contain dictionary atoms that are semantically discriminative & - \\ \cline{2-4}  & [92] & Predict image clicks and solved the problem of lack of data & - \\ \hline \multirow{6}{*}{Topic model based methods} & [94] & Generate visualized summaries & Lack of personalized microblog summarization \\ \cline{2-4}  & [96] & exploit the multimodality and suitable for large-scale data & Without videos and audios modality \\ \cline{2-4}  & [97] & Exploit various property jointly and classify multi-class events & - \\ \cline{2-4}  & [98] & Can classify the visual-representative topics from non-visual-representative topics & Didn’t consider different domains \\ \hline \multirow{6}{*}{Deep learning based methods} & [99] & restrains the negative influence of noisy or irrelevant concepts & - \\ \cline{2-4}  & [100] & Maintain multi-view information by robust representation & - \\ \cline{2-4}  & [101] & A generic model to describe events and their relationships & - \\ \hline \multirow{6}{*}{Other Methods} & [102] & Deal with multi-view tasks & Poor data quality and high complexity \\ \cline{2-4}  & [103] & Jointly regularizing the encoded representations & Lack of event summarization and itemization \\ \cline{2-4}  & [104] & Retrieval structured event representation, robust and effective & - \\ \cline{2-4}  & [105] & Structured topic representation & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages and disadvantages of the multi-modality event detection methods. There are many works researching how to model the event evolution within multi-modality data. Previous evolution modeling methods are mainly focused on a single modality such as text and video/image. However, multi-modality data are very different from single modality data. So, the evolution of multi-modality event evolution is a great challenge and needs completely different methods. [PERSON] et al. [106] proposed to improve news video search by using the semantics of video and relevant external information resources, and then discover topic hierarchy for browsing key events and supporting question answering (QA). The work combined multimodal event information extracted from various online platforms for event evolution analysis and introduced topic evolution based on the interest of users. What is more, they extended QA to deal with the various types of specialized video queries.