  We present an efficient and effective automatic method for determining the
research focus of scientific communities found in co-authorship networks. It
utilizes bibliographic data from a database to form the network, followed by
fastgreedy community detection to identify communities within large connected
components of the network. Text analysis techniques are used to identify
community-specific significant terms which represent the topic of the
community. In order to greatly reduce computation time, the `Topics' field of
each publication in the network is analyzed rather than its entire text. Using
this text analysis approach requires a certain level of statistical
confidence,therefore analyzing very small communities is not effective with
this technique. We find a minimum community size threshold of 8 coauthored
papers; below this value, the community's topic cannot be reliably identified
with this method. Additionally, we consider the implications this study has
regarding factors involved in the formation of scientific communities in
co-authorship networks.
