Activity of non-parliamentary opposition communities in social networks in the context of the Russian 2016 parliamentary election

Myagkov, Mikhail; Shchekotin, Evgeniy V.; Kashpur, Vitaliy V.; Goiko, Vyacheslav L.; Baryshev, Alexey A.

doi:10.1080/21599165.2018.1532411

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…Secondly, next to representation of the enemy we see a utopic picture of the 'correct society', of the way life should be. This representation can be secular and retrospective, with memories of the Russian Empire or the USSR, or it can be a representation of some early anarchist pre-state relations [10].…”

Section: Discussionmentioning

confidence: 99%

Social network big data analysis as a tool for preventing extremism on the Internet in the interests of sustainable development

Thaqi¹,

Shchekotin

Kaznacheev

et al. 2020

E3S Web Conf.

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The crisis caused by the COVID-19 pandemic, as well as the migration policies of various countries around the world, lead to the radicalization of the most marginal social groups, including right - wing extremists. Due to the development of information technologies, right-wing extremists receive new channels for spreading their destructive ideas. Since the main users of data networks are young people, primarily schoolchildren and students, the prevention of extremism in public social media is particularly relevant. The authors of the article believe that an effective tool for preventing extremism is the collection and processing of data on the activity of right-wing radicals in social networks. Such work, according to the authors, can not only increase the effectiveness of identifying extremists on the Internet, but also establish productive interaction with the main target audience of social media - young people. In the interests of sustainable development, it is necessary to cooperate with authorities, technical specialists, and educational institutions in order to develop a unified policy to counter extremism both in the Sverdlovsk region and in Russian Federation and around the world.

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Section: Discussionmentioning

confidence: 99%

Social network big data analysis as a tool for preventing extremism on the Internet in the interests of sustainable development

Thaqi¹,

Shchekotin

Kaznacheev

et al. 2020

E3S Web Conf.

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“…With social networks growing exponentially, problems related to detection of extremist content, recruitment of supporters of right-wing radicals and Islamists, and radicalization of extremist sentiments have attracted the attention of a large number of researchers (Ashcroft et al, 2015; Gitari et al, 2015; Gröndahl et al, 2018; Hartung et al, 2017; Myagkov et al, 2018; Scanlon and Gerber, 2014; Schmidt and Wiegand, 2017; Ting et al, 2013; Wei et al, 2016). However, the overwhelming majority of studies in this area focus on investigating approaches to identification of various forms of extremist discourse; only a few studies address identification of strategies and tactics that enable extremist ideology supporters (right-wing radicals and Islamists) to successfully propagate their ideas on social networks and to recruit new followers.…”

Section: Methods and Datamentioning

confidence: 99%

A comparative analysis of right-wing radical and Islamist communities’ strategies for survival in social networks (evidence from the Russian social network VKontakte)

Myagkov

Shchekotin

Chudinov

et al. 2019

Media, War & Conflict

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This article presents a comparative analysis of online communities of right-wing radicals and Islamists, who are considered to be numerous and dangerous extremist groups in Russian society. The online communities were selected based on the content posted on the largest Russian social networking site VKontakte. The goal of this article is to determine the strategy and tactics employed by extremist online communities for survival on social networking sites. The authors discovered that both right-wing radical and Islamist groups employ similar behavioural techniques, with the mimicry of ideologically neutral content as the most common. In addition, every extremist community also applies some unique methods. For example, if there is a risk of being blocked, right-wing radicals tend to shift their activity and communication to the other internet-based platforms that are not under state control; however, Islamists prefer to suddenly change the content of their communities (i.e. by using secondary mimicry).

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“…Gradient boosting) -метод машинного обучения для задач регрессии и классификации, который создает алгоритм прогнозирования в форме последовательного набора слабых предсказывающих моделей.11 Случайный лес (англ. Random forest) -метод машинного обучения, создающий алгоритм прогнозирования в виде случайного набора решающих деревьев.12 LightGBM (https://github.com/Microsoft/LightGBM) -бесплатная реализация метода градиентного бустинга.13 В своих предыдущих работах мы успешно апробировали методику расчета индекса онлайн-активности для изучения политических процессов[Щекотин и др., 2017;Myagkov et al, 2018;Myagkov et al, 2019].…”

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Субъективная Оценка (Не)благополучия Населения Регионов РФ На Основе Данных Социальных Сетей

Щекотин¹,

Мягков²,

Goiko³

et al. 2019

MONITORING OF PUBLIC OPINION

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В статье обосновывается новый метод субъективной оценки благополучия, основанный на анализе онлайн-активности пользователей в социальных сетях. Предлагаемый метод обладает как преимуществами (быстрота исследования, небольшие затраты, масштабность, детальность полученной информации), так и определенными ограничениями (охват «цифрового населения», технические сложности при исследовании густонаселенных мегаполисов и т.д.). В статье представлены результаты эмпирического исследования онлайн-активности пользователей социальной сети «ВКонтакте». На основе полученных данных рассчитан индекс субъективного (не)благополучия для 43 регионов РФ по 19 показателям, охватывающих экономические, социальные и политические аспекты качества жизни людей. Индекс субъективного (не)благополучия строится на изучении онлайн-активности пользователей, входящих в 1350 наиболее популярных региональных и городских сообществ в социальной сети «ВКонтакте». Период исследования включает в себя весь 2018 год. Благодарность. Исследование выполнено при финансовой поддержке «Программы повышения конкурентоспособности Томского государственного университета» (грант № 8.1.59.2018).

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Activity of non-parliamentary opposition communities in social networks in the context of the Russian 2016 parliamentary election

Cited by 8 publications

References 24 publications

Social network big data analysis as a tool for preventing extremism on the Internet in the interests of sustainable development

Social network big data analysis as a tool for preventing extremism on the Internet in the interests of sustainable development

A comparative analysis of right-wing radical and Islamist communities’ strategies for survival in social networks (evidence from the Russian social network VKontakte)

Субъективная Оценка (Не)благополучия Населения Регионов РФ На Основе Данных Социальных Сетей

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