  There has been an increasingly widespread agreement among both academic
circles and the general public that the Social Media Platforms (SMPs) play a
central role in the dissemination of harmful and negative sentiment content in
a coordinated manner. A substantial body of recent scholarly research has
demonstrated the ways in which hateful content, political propaganda, and
targeted messaging on SMPs have contributed to serious real-world consequences.
Adopting inspirations from graph theory, in this paper we apply novel network
and community finding algorithms over a representative Facebook dataset
(n=608,417) which we have scrapped through 630 pages. By applying Girvan-Newman
algorithm over the historical dataset our analysis finds five communities of
coordinated networks of actors, within the contexts of Indian far-right
Hindutva discourse. This work further paves the path for future potentials of
applying such novel network analysis algorithms to SMPs, in order to
automatically identify toxic coordinated communities and sub-communities, and
to possibly resist real-world threats emerging from information dissemination
in the SMPs.
