figure 2. Connections among twitter users who recently mentioned GoP when queried on July 25, 2011, with vertices scaled by numbers of followers. the clusters are created by the patterns of connections (follows, replies, and mentions) among the authors in the graph. the clusters were based on Clauset-newman-moore algorithmic analysis in which the red cluster is composed of largely GoP supporters, while the blue cluster contains largely critics and opponents of the GoP as indicated by the content of the tweets from each cluster. other colored or shaped nodes are not strongly affiliated with either major cluster. Users on the bottom are not connected with any of the other twitter users.

 

will benefit from coupling with natural language processing and discourse analysis to identify nexuses of positive collaborations as well as threatening activity from hate groups, terrorists, and criminals (see Figure 2).

Still more ambitious research goals
are to identify key influencers, success-
ful discussion generators, and reliable
answer providers in discussion groups
with millions of participants while
curbing the damage caused by scam-
mers, spammers, and troublemakers
of many kinds who seek to undermine
the efficacy of social media platforms
(see http://www.wikitrust.net).

Broad scholarly Payoffs Not every computing scientist will be interested in studying social media, but computing science social media research can have a profound impact on every discipline. Social media