E3S Web Conf.
Volume 208, 2020First Conference on Sustainable Development: Industrial Future of Territories (IFT 2020)
|Number of page(s)||5|
|Section||Sustainable Development Legislation and Policy|
|Published online||24 November 2020|
Social network big data analysis as a tool for preventing extremism on the Internet in the interests of sustainable development
1 University ‘Kadri Zeka’, Zija Shemsiu Str., 60000 Gjilan, Kosovo
2 Novosibirsk State University of Economics and Management, Kamenskaya Str., 56, 630099 Novosibirsk, Russia
3 Siberian State University of Telecommunications and Information Sciences, Kirova Str., 86, 630102 Novosibirsk, Russia
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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