Method for Detecting Far-Right Extremist Communities on Social Media

Anna Karpova, Aleksei Savelev, Alexander Vilnin and Sergey Kuznetsov
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Anna Karpova: Division for Social Sciences and Humanities, School of Core Engineering Education, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia
Aleksei Savelev: Division for Information Technology, School of Computer Science & Robotics, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia
Alexander Vilnin: Division for Information Technology, School of Computer Science & Robotics, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia
Sergey Kuznetsov: Division for Information Technology, School of Computer Science & Robotics, National Research Tomsk Polytechnic University, 634050 Tomsk, Russia

Social Sciences, 2022, vol. 11, issue 5, 1-19

Abstract: Far-right extremist communities actively promote their ideological preferences on social media. This provides researchers with opportunities to study these communities online. However, to explore these opportunities one requires a way to identify the far-right extremists’ communities in an automated way. Having analyzed the subject area of far-right extremist communities, we identified three groups of factors that influence the effectiveness of the research work. These are a group of theoretical, methodological, and instrumental factors. We developed and implemented a unique algorithm of calendar-correlation analysis (CCA) to search for specific online communities. We based CCA on a hybrid calendar correlation approach identifying potential far-right communities by characteristic changes in group activity around key dates of events that are historically crucial to those communities. The developed software module includes several functions designed to automatically search, process, and analyze social media data. In the current paper we present a process diagram showing CCA’s mechanism of operation and its relationship to elements of automated search software. Furthermore, we outline the limiting factors of the developed algorithm. The algorithm was tested on data from the Russian social network VKontakte. Two experimental data sets were formed: 259 far-right communities and the 49 most popular (not far-right) communities. In both cases, we calculated the type II error for two mutually exclusive hypotheses—far-right affiliation and no affiliation. Accordingly, for the first sample, β = 0.81. For the second sample, β = 0.02. The presented CCA algorithm was more effective at identifying far-right communities belonging to the alt-right and Nazi ideologies compared to the neo-pagan or manosphere communities. We expect that the CCA algorithm can be effectively used to identify other movements within far-right extremist communities when an appropriate foundation of expert knowledge is provided to the algorithm.

Keywords: online radicalization; far-right; extremism; terrorism; social media analytics; big data; web mining (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2022
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