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.

翻译：学术界和公众日益普遍达成共识：社交媒体平台在协调传播有害和负面情绪内容方面扮演着核心角色。近期大量学术研究表明，社交媒体上的仇恨内容、政治宣传和定向信息传播已导致严重的现实后果。本文受图论启发，在通过630个页面抓取的具有代表性的Facebook数据集（n=608,417）上，应用新型网络与社群发现算法。通过对历史数据集应用吉文-纽曼算法，我们的分析在印度极右翼印度教民族主义话语背景下，识别出由行为者协调网络构成的五个社群。本研究进一步开拓了将此类网络分析算法应用于社交媒体的未来潜力，以实现自动识别有害协调社群及子社群，并可能抵御源自社交媒体信息传播的现实威胁。