Identifying and analysing toxic actors and communities on Facebook by employing network analysis

TL;DR

This paper uses advanced network analysis algorithms on Facebook data to identify coordinated toxic communities, specifically within Indian far-right discourse, highlighting the potential for automated detection of harmful online groups.

Contribution

It introduces novel network and community detection algorithms applied to Facebook data to identify toxic, coordinated communities, demonstrating the approach on Indian far-right discourse.

Findings

Identified five coordinated communities within Facebook data.

Applied Girvan-Newman algorithm for community detection.

Showcased potential for automatic toxic community detection.

Abstract

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.