Abstract:With the exponential growth of online social networks， the visual analysis technique has become an important research issue for understanding social media and social network users. In this article, targeting at social media, social network users, and their interactions respectively, we provide a detailed survey on research work of social media topic analysis, social user modeling, and personalized social information retrieval. We hope these techniques can be utilized to improve the Web user experience and the national security service. Recent research work in visualizing social media are divided into：（1）topic analysis;（2） topic diversification; and（3） topic evolution. Topic analysis means to construct a group of themes from a set of documents, and assign each document with a theme or a group of themes. Typical methods include the Probabilistic Latent Semantic Indexing（PLSI）, the Latent Dirichlet Allocation（LDA）, etc. Topic diversification refers to distinguish different types of topics. For example, some topics are objective contents, and other topics are subjective sentiments. In order to achieve this, supervised topic models are proposed to identify different topic types. Topic evolution is to connect topics in consecutive time intervals through topic similarity measures, through which one can see the topic change, the topic merging, and the topic separation. Generally, KL-divergence is utilized as the similarity measure. The work in visualizing social network users are classified into：（1） single-domain user preference modeling;（2） cross-domain preference transformation; and（3） user preference evolution. Single-domain user preference modeling is to describe a user’ s personal bias on items in a single domain. For example, to model a user’ s preference in a movie review system. Typical solutions are to combine the review aspect modeling with traditional collaborative filtering, in which the aspect is utilized for preference description. Cross-domain preference transformation is to learn the user preference pattern through multiple domains. In a typical scenario, a user has some feedbacks in a movie review site, but has very few feedbacks in a book site. By transferring learning techniques, the movie feedback is utilized to inference the book preference. User preference evolution refers to distinguishing a user’ s interests in different time periods. For example, a user may pay attention to furniture when he/she moves to a new house, but he/she may be interested in milk powder when he/she has a baby. By modeling the evolution patterns, the interests in the next time period can be predicted. The work in visualizing the interactions between social media and users are grouped into：（1）personalized search;（2） personalized recommendation; and（3）search/recommendation results reconstruction. Personalized search means to return search results by considering a user’ s search history. Thus a user model is constructed in advance, and the displayed search results are required to match the user’ s intent. Personalized recommendation is to retrieve information without queries. Usually, collaborative filtering models are utilized to match a user’ s interest and the relevant items. In visualizing the items to the user, the diversity among a group of displayed items needs to be considered. Therefore, some entropy-based objectives are proposed to be joint with the traditional collaborative filtering objectives. Search/recommendation results reconstruction refers to convert unstructured retrieval results into structured data automatically. This will make both conveniences for user experiences and search engine indexing. Some future work directions are also discussed in the end, such as visualizing temporal-spatial properties of social network, visualizing the combination of internet of things, mobile internet, traditional internet, etc.