Making Sense of Social Events by Event monitoring, Visualization and Underlying Community Profiling

With the prevalence of intelligent devices, social networks have been playing an increasingly important role in our daily life. Various social networks (e.g., Twitter, Facebook) provide convenient platforms for users to explore the world. In this thesis, we study the problem of multi-perspective analysis of social events detected from social networks. In particular, we aim to make sense of the social events from the following three perspectives: 1) what are these social events about; 2) how do these events evolve along timeline; 3) who are involved in the discussions on these events. We mainly work on two categories of social data: the user-generated contents such as tweets and Facebook posts, and the users' interactions such as the follow and reply behaviours among users. On one hand, the posts reveal valuable information that describes the evolutions of miscellaneous social events, which is crucial for people to understand the world. On the other hand, users' interactions demonstrate users' relationships among each other and thus provide opportunities for analysing the underlying communities behind the social events. However, it is not practical to manually detect social events, monitor event evolutions or profile the underlying communities from the massive amount of social data generated everyday. Hence, how to efficiently and effectively extract, manage and analyse the useful information from the social data for multi-perspective social events understanding is of great importance. The social data is dynamic source of information which enables people to stay informed of what is happening now and who are the active and influential users discussing these social events. For one thing, social data is generated by people worldwide at all time, which may make fast identification of events even before the mainstream media. Moreover, the continuous stream of social data reflects the event evolutions and characterizes the events with changing opinions at different stages. This provides an opportunity to people for timely responses to urgent events. For another, users are often not isolated in social networks. The interactions between users can be utilized to discover the communities who discuss each social event. Underlying community profiling provides answers to the questions like who are interested in these events, and which group of people are the most influential users in spreading certain event topics. These answers deepen our understanding of the social events by considering not only the events themselves but also the users behind these events. The first research task in this thesis is to monitor and index the evolving events from social textual contents. The social data cover a wide variety of events which typically evolve over time. Although event detection has been actively studied, most existing approaches do not track the evolution of events, nor do they address the issue of efficient monitoring in the presence of a large number of events. In this task, we detect events based on the user-generated textual contents and design four event operations to capture the dynamics of events. Moreover, we propose a novel event indexing structure, called Multi-layer Inverted List, to manage dynamic event databases for the acceleration of large-scale event search and update. The second research task is to explore multiple features for social events tracking and visualization. In addition to textual contents utilized in the first task, social data contains various features, such as images and timestamps. The benefits of incorporating different features into event detection are twofold. First, these features provide supplemental information that facilitates the event detection model. Second, different features describe the detected events from different aspects, which enables users to have a better understanding with more vivid visualizations. To improve the event detection performance, we propose a novel generative probabilistic model which jointly models five different features. The event evolution tracking is achieved by applying the maximum-weighted bipartite graph matching on the events discovered in consecutive periods. Events are then visualized by the representative images selected based on our three defined criteria. The third research task is to detect and profile the underlying social communities in social events. The social data not only contains user-generated contents which describe the events evolutions, but also comprises various information on the users who discuss these events, such as user attributes, user behaviours, and so on. Comprehensively utilizing this user information can help to group similar users into communities, and enrich the social event analysis from the community perspective. Motivated by the rich semantics about user behaviours hidden in social data, we extend the community definition as a group of users who are not only densely connected, but also having similar behaviours. Moreover, in addition to detecting the communities, we further profile each of the detected communities for social events analysis. A novel community profiling model is designed to detect and characterize a community by both content profile (what a community is about) and diffusion profile (how it interacts with others)