  Online social media (OSM) has a enormous influence in today's world. Some
individuals view OSM as fertile ground for abuse and use it to disseminate
misinformation and political propaganda, slander competitors, and spread spam.
The crowdturfing industry employs large numbers of bots and human workers to
manipulate OSM and misrepresent public opinion. The detection of online
discussion topics manipulated by OSM \emph{abusers} is an emerging issue
attracting significant attention. In this paper, we propose an approach for
quantifying the authenticity of online discussions based on the similarity of
OSM accounts participating in the discussion to known abusers and legitimate
accounts. Our method uses several similarity functions for the analysis and
classification of OSM accounts. The proposed methods are demonstrated using
Twitter data collected for this study and previously published \emph{Arabic
honeypot dataset}. The former includes manually labeled accounts and abusers
who participated in crowdturfing platforms. Evaluation of the topic's
authenticity, derived from account similarity functions, shows that the
suggested approach is effective for discriminating between topics that were
strongly promoted by abusers and topics that attracted authentic public
interest.
