@conference {4111,
	title = {The Creation and Distribution of Social Capital in Twitter Conversations among Teachers {\textendash} A Longitudinal Ego-Network Approach},
	booktitle = {XXXV. Sunbelt Social Networks Conference },
	year = {2015},
	month = {06/2015},
	address = {Brighton, UK},
	abstract = {Social networking sites (SNS) provide a ready-made environment to acquire and share new information, while collaborating in social networks of diverse groups of people. This study focuses on the creation and distribution of social capital within SNS, such as Twitter. Social capital has been repeatedly proposed as a valuable theoretical concept to analyze SNS. Moreover, previous research has greatly contributed to our understanding of how individuals communicate via Twitter. Yet, past studies remained inconclusive about the precise role of social capital in SNS. Additionally, little is known about whether and how teachers use SNS (e.g. Twitte) to share and collect information. The present study addresses these shortcomings by providing empirical evidence from two hashtag conversations, namely $\#$edchat and $\#$edchatde. Both aim at teachers and cover the latest trends and developments in the field of (new) media in teaching and learning processes. We collected longitudinal ego-network data from 22.05. {\textendash} 06.11.2014, yielding more than 144,000 Tweets from about 52,000 Twitter users. Building upon the work of Tsai and Goshal (1998), who acknowledged social network analysis as a valuable tool to assess (aspects of) of social capital, we determined egos{\textquoteright} betweenness and closeness centrality measures. Moreover, we also determined their hub and authority scores (Kleinberg, Kumar, Raghavan, Rajagopalan, \& Tomkins, 1999), as well as brokerage roles (De Nooy, Mrvar, \& Batagelj, 2011). Finally, we also assessed the existence and relevance of structural holes (e.g. Burt, 2009) in the emerging network structures of the hashtag conversations. The results show that there are overarching similarities among the Twitter conversations on how social capital is created and distributed. However, we also revealed decisive differences with respect to brokerage roles, as well as hub and authority scores. Based on these findings we will formulate recommendations for future studies, as well as discuss preliminary implications for practitioners.},
	keywords = {ego-network, Longitudinal Analysis, social capital, Social Network Analysis},
	author = {Martin Rehm and Michael Kerres}
}
