Matthew S. Smith

Identifying Health-Related Topics on Twitter

Public health-related topics are difficult to identify in large conversational datasets like Twitter. This study examines how to model and discover public health topics and themes in tweets. Tobacco use is chosen as a test case to demonstrate the effectiveness of topic modeling via LDA across a large, representational dataset from the United States, as well as across a smaller subset that was seeded by tobacco-related queries. Topic modeling across the large dataset uncovers several public health-related topics, although tobacco is not detected by this method. However, topic modeling across the tobacco subset provides valuable insight about tobacco use in the United States. The methods used in this paper provide a possible toolset for public health researchers and practitioners to better understand public health problems through large datasets of conversational data.

Implicit affinity networks and social capital

Social networks are typically constructed based on explicit and well-defined relationships among individuals. In this paper, we describe another class of social networks, known as implicit affinity networks, where links are implicit in the patterns of natural affinities among individuals. An effective mathematical formulation of social capital based on implicit and explicit connections is given. Results with two Web communities, one focused on people’s interests and one focused on people’s blogs, exhibit rich dynamics and show interesting patterns of community evolution.

Bonding vs. Bridging Social Capital: A Case Study in Twitter

Online communities are connecting large numbers of individuals and generating rich social network data, opening the way for empirical studies of social behavior. In this paper, we consider the widely-held view of social scientists that bonding interactions are more likely than bridging interactions in social networks, and test it within the context of the large online Twitter community. We find that indeed users who request to follow others having similar profile descriptions (i.e., attempting to bond) increase the number of Twitter users who reciprocate their follow requests. From a practical standpoint, this result also informs how a new user might interact on Twitter to maintain a high follow-back ratio.

Social capital in online communities

Online communities are connecting hordes of individuals and generating rich social network data. The social capital that resides within these networks is largely unknown. We propose to create a mathematical model of social capital that incorporates the mobilization of social resources through purposive actions. This includes evaluating nodes based not only on their relationships and attributes, but on their social resources as well. Investigating the costs associated with reciprocally connecting to individuals will also be assessed. The result is a quantitative model for characterizing and providing decision support on how to maximize participation within social networks.