Book Description

Social media platforms such as Twitter, Facebook, and blogs have emerged as valuable- in fact, the de facto - virtual town halls for people to discover, report, share and communicate with others about various types of events. These events range from widely-known events such as the U.S Presidential debate to smaller scale, local events such as a local Halloween block party. During these events, we often witness a large amount of commentary contributed by crowds on social media. This burst of social media responses surges with the "second-screen" behavior and greatly enriches the user experience when interacting with the event and people's awareness of an event. Monitoring and analyzing this rich and continuous flow of user-generated content canyield unprecedentedly valuable information about the event, since these responses usually offer far more rich and powerful views about the event that mainstream news simply could not achieve. Despite these benefits, social media also tends to be noisy, chaotic, and overwhelming, posing challenges to users in seeking and distilling high quality content from that noise. In this dissertation, I explore ways to leverage social media as a source of information and analyze events based on their social media responses collectively. I develop, implement and evaluate EventRadar, an event analysis toolbox which is able to identify, enrich, and characterize events using the massive amounts of social media responses. EventRadar contains three automated, scalable tools to handle three core event analysis tasks: Event Characterization, Event Recognition, and Event Enrichment. More specifically, I develop ET-LDA, a Bayesian model and SocSent, a matrix factorization framework for handling the Event Characterization task, i.e., modeling characterizing an event in terms of its topics and its audience's response behavior (via ET-LDA), and the sentiments regarding its topics (via SocSent). I also develop DeMa, an unsupervised event detection algorithm for handling the Event Recognition task, i.e., detecting trending events from a stream of noisy social media posts. Last, I develop CrowdX, a spatial crowdsourcing system for handling the Event Enrichment task, i.e., gathering additional first hand information (e.g., photos) from the field to enrich the given event's context. Enabled by EventRadar, it is more feasible to uncover patterns that have not been explored previously and re-validating existing social theories with new evidence. As aresult, I am able to gain deep insights into how people respond to the event that they are engaged in. The results reveal several key insights into people's various responding behavior over the event's timeline such the topical context of people's tweets does not always correlate with the timeline of the event. In addition, I also explore the factors that affect a person's engagement with real-world events on Twitter and find that people engage in an event because they are interested in the topics pertaining to that event; and while engaging, their engagement is largely affected by their friends'behavior.