Method for Detecting Far-Right Extremist Communities on Social Media
| Parent link: | Social Sciences Vol. 11, iss. 5.— 2022.— [200, 20 p.] |
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| Enti autori: | , |
| Altri autori: | , , , |
| Riassunto: | Title screen 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, Я = 0.81. For the second sample, Я = 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. |
| Lingua: | inglese |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | http://earchive.tpu.ru/handle/11683/73246 https://doi.org/10.3390/socsci11050200 |
| Natura: | Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=668201 |
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| 200 | 1 | |a Method for Detecting Far-Right Extremist Communities on Social Media |f A. Yu. Karpova, A. O. Savelyev, S. A. Kuznetsov, A. D. Vilnin | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 63 tit.] | ||
| 330 | |a 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, Я = 0.81. For the second sample, Я = 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. | ||
| 461 | |t Social Sciences | ||
| 463 | |t Vol. 11, iss. 5 |v [200, 20 p.] |d 2022 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a online radicalization | |
| 610 | 1 | |a far-right | |
| 610 | 1 | |a extremism | |
| 610 | 1 | |a terrorism | |
| 610 | 1 | |a social media analytics | |
| 610 | 1 | |a big data | |
| 610 | 1 | |a web mining | |
| 610 | 1 | |a радикализация | |
| 610 | 1 | |a экстремизм | |
| 610 | 1 | |a терроризм | |
| 610 | 1 | |a социальные сети | |
| 610 | 1 | |a большие данные | |
| 701 | 1 | |a Karpova |b A. Yu. |c philosopher |c Professor of Tomsk Polytechnic University, Doctor of Social Sciences |f 1968- |g Anna Yurievna |3 (RuTPU)RU\TPU\pers\32542 |9 16464 | |
| 701 | 1 | |a Savelyev |b A. O. |c Specialist in the field of informatics and computer technology |c Associate Professor of Tomsk Polytechnic University, Candidate of Technical Sciences |f 1987- |g Aleksey Olegovich |3 (RuTPU)RU\TPU\pers\31388 |9 15560 | |
| 701 | 1 | |a Kuznetsov |b S. A. |c specialist in the field of information technology |c Engineer of Tomsk Polytechnic University |f 1985- |g Sergey Anatoljeich |3 (RuTPU)RU\TPU\pers\47274 | |
| 701 | 1 | |a Vilnin |b A. D. |c Specialist in the field of automation equipment and electronics |c The Head of the Laboratory of Tomsk Polytechnic University |f 1980- |g Alexander Daniilovich |3 (RuTPU)RU\TPU\pers\45840 | |
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| 856 | 4 | |u https://doi.org/10.3390/socsci11050200 | |
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