Method for Detecting Far-Right Extremist Communities on Social Media
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Social Sciences |
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| Online Access: | https://www.mdpi.com/2076-0760/11/5/200 |
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| author | Anna Karpova Aleksei Savelev Alexander Vilnin Sergey Kuznetsov |
| author_facet | Anna Karpova Aleksei Savelev Alexander Vilnin Sergey Kuznetsov |
| author_sort | Anna Karpova |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:51:01Z |
| format | Article |
| id | doaj.art-6bc214d058c84e64b515c800f6ff898f |
| institution | Directory Open Access Journal |
| issn | 2076-0760 |
| language | English |
| last_indexed | 2024-03-10T01:51:01Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Social Sciences |
| spelling | doaj.art-6bc214d058c84e64b515c800f6ff898f2023-11-23T13:05:07ZengMDPI AGSocial Sciences2076-07602022-05-0111520010.3390/socsci11050200Method for Detecting Far-Right Extremist Communities on Social MediaAnna Karpova0Aleksei Savelev1Alexander Vilnin2Sergey Kuznetsov3Division for Social Sciences and Humanities, School of Core Engineering Education, National Research Tomsk Polytechnic University, 634050 Tomsk, RussiaDivision for Information Technology, School of Computer Science & Robotics, National Research Tomsk Polytechnic University, 634050 Tomsk, RussiaDivision for Information Technology, School of Computer Science & Robotics, National Research Tomsk Polytechnic University, 634050 Tomsk, RussiaDivision for Information Technology, School of Computer Science & Robotics, National Research Tomsk Polytechnic University, 634050 Tomsk, RussiaFar-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.https://www.mdpi.com/2076-0760/11/5/200online radicalizationfar-rightextremismterrorismsocial media analyticsbig data |
| spellingShingle | Anna Karpova Aleksei Savelev Alexander Vilnin Sergey Kuznetsov Method for Detecting Far-Right Extremist Communities on Social Media Social Sciences online radicalization far-right extremism terrorism social media analytics big data |
| title | Method for Detecting Far-Right Extremist Communities on Social Media |
| title_full | Method for Detecting Far-Right Extremist Communities on Social Media |
| title_fullStr | Method for Detecting Far-Right Extremist Communities on Social Media |
| title_full_unstemmed | Method for Detecting Far-Right Extremist Communities on Social Media |
| title_short | Method for Detecting Far-Right Extremist Communities on Social Media |
| title_sort | method for detecting far right extremist communities on social media |
| topic | online radicalization far-right extremism terrorism social media analytics big data |
| url | https://www.mdpi.com/2076-0760/11/5/200 |
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