Creating thematic sessions based on accepted papers is important to the success of a conference. Facing a large number of papers from multiple topics, conference organizers need to identify the topics of papers and group them into sessions by considering the constraints on session numbers and paper numbers in individual sessions. In this paper, we present a system using visualization and topic modeling to help the construction of conference sessions. The system provides multiple automatically generated session schemes and allows users to create, evaluate, and manipulate paper sessions with given constraints. A case study based on our system on the VAST papers shows that our method can help users successfully construct coherent conference sessions. In addition to conference session management, our method can be extended to other tasks, such as event and class schedule.

Information diffusion analysis is important in social media. In this work, we present a coherent ego-centric and event-centric model to investigate diffusion patterns and user behaviors. Applying the model, we propose Diffusion Map+ (D-Maps+), a novel visualization method to support exploration and analysis of user behaviors and diffusion patterns through a map metaphor. For ego-centric analysis, users who participated in reposting (i.e., resending a message initially posted by others) one central user’s posts (i.e., a series of original tweets) are collected. Event-centric analysis focuses on multiple central users discussing a specific event, with all the people participating and reposting messages about it. Social media users are mapped to a hexagonal grid based on their behavior similarities and in the chronological order of repostings. With the additional interactions and linkings, D-Map+ is capable of providing visual profiling of influential users, describing their social behaviors and analyzing the evolution of significant events in social media. A comprehensive visual analysis system is developed to support interactive exploration with D-Map+. We evaluate our work with real-world social media data and find interesting patterns among users and events. We also perform evaluations including user studies and expert feedback to certify the capabilities of our method.

Social media data with geotags can be used to track people's movements in their daily lives. By providing both rich text and movement information, visual analysis on social media data can be both interesting and challenging. In contrast to traditional movement data, the sparseness and irregularity of social media data increase the difficulty of extracting movement patterns. To facilitate the understanding of people's movements, we present an interactive visual analytics system to support the exploration of sparsely sampled trajectory data from social media. We propose a heuristic model to reduce the uncertainty caused by the nature of social media data. In the proposed system, users can filter and select reliable data from each derived movement category, based on the guidance of uncertainty model and interactive selection tools. By iteratively analyzing filtered movements, users can explore the semantics of movements, including the transportation methods, frequent visiting sequences and keyword descriptions. We provide two cases to demonstrate how our system can help users to explore the movement patterns.

In this work, we propose a visual analytic system for analyzing events of Weibo, a Chinese-version microblog service. We build a system which consists of two interfaces: a web-based online visualization interface for public users and an offline expert visual analytic system which wraps the online one and provides additional analysis functions. The online interface provides an intuitive and powerful retweet tree visualization which inspires users' creativity. The expert system adopts public users' analysis results collected from the web interface, and can visualize and analyze Weibo events to a deeper extent.