Abstract:
Research shows that cyber-hate, illegal or malicious
form of cyber-protest and cyber-activism in online
social media and Web 2.0 platforms has become a common
phenomenon. This is a growing concern and hence automated
techniques to counter such forms of online propaganda and
identification of users and virtual communities is an area which
has recently attracted a lot of research attention. In this paper,
we present a simple and effective method to mine Twitter
(a very popular and largest micro-blogging service) data for
automatically identifying users and communities having a
shared agenda. We propose a generic framework that consists
of a systematic and focused traversal of the follower-network
on Twitter and a user profile classifier based on contentbased
features. We customize the proposed framework for a
specific domain and demonstrate that the proposed approach is
effective. We perform empirical analysis on data crawled from
Twitter and the experimental results on the test dataset reveals
that the proposed features and framework can successfully
identify twitterers and hidden communities having a common
agenda or shared interest.
