- 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 can
yield 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 a
result, 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.
Social media sites focusing on health-related topics are rapidly gaining popularity among online health consumers, also known as "e-patients". The increasing adoption of social media by e-patients and their demand for reliable health information has prompted several health care organizations (HCOs) to establish their social media presence. HCOs are using social media to connect with current and potential e-patients, and improve patient education and overall quality of care. A significant benefit for HCOs in using social media could potentially be the improvement of their quality of care, as perceived by patients. Perceived quality of care is a key determinant of patients' experience and satisfaction with health care services, and has been a major focus of research. However, there is very little research on the relationship between patients' online social media experience and their perceived quality of care. The objective of this research was to evaluate e-patients' online experience with an HCO's social media sites and examine its impact on their perceived quality of care. Research methodology included a combination of qualitative and quantitative approaches. Data for this study was collected from Mayo Clinic's social media sites through an online survey. Descriptive statistics were used to identify basic demographic profiles of e-patients. Linear regression analysis was used to examine the relationship between online experience and perceived quality of care. Qualitative data was analyzed using thematic analysis. Results showed a positive relationship between online experience and perceived quality of care. Qualitative data provided information about e-patients' attitudes and expectations from healthcare social media. Overall, results yielded insights on design and management of social media sites for e-patients, and integration of these online applications in the health care delivery process. This study is of value to HCOs, health communicators and social media designers, and will also serve as a foundation for subsequent studies in the area of health care social media.