Method for Detecting Far-Right Extremist Communities on Social Media
2.1. The Peculiarities of Far-Right Extremist Communities
- belief in the specific inferiority and the superiority of other individuals and groups; promotion of the segregation principle: the separation of people into groups considered “superior” and groups considered “inferior”, with different bases: gender, age, status, place of residence, race, and others;
- though various far-right communities and movements differ in many ways, they share and promote “national preferences” (hence, nationalism);
- the idea of egalitarianism: the far-right regards social inequalities and corresponding social hierarchies as inevitable, natural, or even preferable;
- the broad landscape we call the far-right relies on supremacism and nativism;
- promotion of oppressive policies, genocide, xenophobia, authoritarianism, anti-immigration and anti-integration attitudes;
- many far-right groups believe in conspiracy theories as a severe threat to national sovereignty and (or) personal freedom, and they also maintain the conviction that their personal and (or) national way of life is under threat.
2.2. Far-Right Online Radicalization: Specifics of the Study
2.3. Key Opportunities and Limitations in the Creation of Automated Online Radicalization Research Tools
- Data extraction–level limitations. The primary way to extract raw social media data is to work with application programming interfaces (APIs) developed by social media owners via data provision methods. The rules for API use are set by the social media themselves, including the permissible frequency of requests, the amount of data provided in response to the request, and others. Using several social media as primary data sources entails multi-agent acquisition subsystem development. Apart from being technically challenging, especially for small research teams, it also means that we depend entirely on social media owners;
- Data processing–level limitations. Despite the extraordinary amount of publicly available data on social media, the amount is still insufficient. There is no explicit information about the nature of the connections between users and communities. There is no possibility of verifying the available information (as a consequence, it is impossible to evaluate the accuracy of models based on machine learning methods) (Tang and Liu 2010). Thus, we are in a situation where we cannot ignore the available online information as it can potentially improve the accuracy of the scientific worldview, but we also cannot base decisions solely on online data;
- Data interpretation–level limitations. The development of artificial intelligence methods and their accompanying use increases the qualification requirements for researchers. Additional competence in development programs and new educational trajectories are necessary in this case, but a qualitative formalization of accumulated experience and knowledge allows the transit to algorithm development.
2.4. Far-Right Extremist Communities in Russia
- There was a decline in criminal activity but a growing share of more dangerous violence;
- Hate crimes have become even more concealed;
- At least 45 people suffered from racist and other ideologically motivated violence;
- The number of right-wing attacks on political, ideological, or “stylistic” opponents was significantly lower than the year before;
- The number of attacks on ideological sites decreased;
- The proportion of dangerous acts—explosions and arson—increased during the year;
- The theme of the threats from the far-right remained topical; photos, personal data of anti-fascists, left-wing activists, independent journalists, and law enforcement officers, and threats against them appeared on the social media pages of these organizations and groups.
2.5. Specific Markers for the Promotion of Far-Right Extremist Ideology in the Online Environment
- The construction of a collective identity to maintain group cohesion and attract new members;
- Extrapolation of radical prejudices (e.g., racism) into “rational” claims focused on ethnic, national, linguistic, and religious minorities;
- Funding of individual values and motives that can stimulate active involvement in far-right communities;
- Seeking significance and status;
- Networking with like-minded individuals for offline and online mobilization and recruiting new members;
- The crucial role of ideology in justifying violent action;
- Charismatic leadership as a stimulus to increase organizational strength;
- Background conditions—social, political, economic, and others.
3. The Architecture of the VKontakte Social Network Analysis System
4. The Calendar-Correlation Analysis (CCA) Algorithm of Social Network Community Activity
- It is not possible to extract retrospective data on community activity, and it is only possible to estimate the total number of views, likes, reposts, and comments since the publication date of the post;
- The community may show abnormal activity compared to regular activity before a significant date and after;
- Community activity on significant dates can be random or standard, and this is the case not only for radical communities.
- Aabs—absolute community activity, which is a superposition of views, likes, comments, and reposts;
- d—an important date for the far-right ideological platforms;
- k—a variable to denote the boundaries of the time interval in question.
- The expert user builds a “knowledge base” (a list of keywords, expressions, and dates, including defining their relationships);
- The user launches the primary keyword search function;
- Pre-processing of the results (deleting closed, inactive, “empty” communities);
- The method of calendar-correlation analysis is used to refine the list of identified far-right communities;
- The expert analyzes the results and enters them into the knowledge base, if necessary;
- The user builds a set of groups for further search of related communities (satellites);
- The results of the search for the satellite communities are analyzed. If necessary, the expert refines the information in the knowledge base.
5. Experimental Testing of CCA and Results Discussion
Data Availability Statement
Conflicts of Interest
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|Subclass||β||1 − β||Number of Communities within the Dataset|
|Median||Average||Not Specified, |
|Male||Female||Over the Year||Daily Average|
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Karpova, A.; Savelev, A.; Vilnin, A.; Kuznetsov, S. Method for Detecting Far-Right Extremist Communities on Social Media. Soc. Sci. 2022, 11, 200. https://doi.org/10.3390/socsci11050200
Karpova A, Savelev A, Vilnin A, Kuznetsov S. Method for Detecting Far-Right Extremist Communities on Social Media. Social Sciences. 2022; 11(5):200. https://doi.org/10.3390/socsci11050200Chicago/Turabian Style
Karpova, Anna, Aleksei Savelev, Alexander Vilnin, and Sergey Kuznetsov. 2022. "Method for Detecting Far-Right Extremist Communities on Social Media" Social Sciences 11, no. 5: 200. https://doi.org/10.3390/socsci11050200