Feb. 17, 11:45, Wei Ji Ma. Optimality and Probabilistic Computation in Visual Categorization

Mor Naaman

School of Communication and Information, Rutgers

Large Scale Studies of Social Information on Twitter

Online social networks, articulated through many of the today's popular Web applications such as Facebook and Twitter, provide people with ways to create connections, seek support, share ideas, form and maintain relationships, and receive information. These environments expose a rich set of connections and interactions between individuals that allow us to examine, in large scale and natural settings, various sociological phenomena that have previously been difficult to study. Such investigations can show whether, and how, social theories play out in the new settings; can help expand and suggest new theories or directions for future investigation; and help us reason about social media systems and their design.  

First, we look at how network properties relate to persistence of ties on Twitter. Building on concepts and theories from sociology such as strength of ties, embeddedness, and status, we examine how network structure is associated with the breaking of ties between individuals in Twitter's directed social network. We investigate this "unfollowing" phenomenon using a set of 245,000 Twitter edges, and the persistence or disappearance of these edges after nine months. Our analysis suggests that structural properties of the network have a significant association with the persistence of ties on Twitter, and helps us reason in a new way about status and power intrinsic to Twitter.

Second, we examine theories of gender and communication in Twitter's social interactions, where we can observe personal exchanges in natural, semi-public settings. Using 78,000 “reply” messages exchanged between 1753 gender-coded pairs of Twitter users, we study the relationship between gender composition and language use, while controlling for the strength of connection between the conversing users. Results are in line with previous findings and theories from sociolinguistic and communication, for example, showing that  women express more positive emotion, and use more intensifier adverbs and pronouns, especially when communicating with other women. This initial investigation helps the understanding of gender-driven communication patterns in social media, and raises questions about how these communication tendencies are heightened in semi-public settings. 
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