Feature Selection for Intelligent Detection of Targeted Influence on Public Opinion in Social Networks

Nowadays social networks become the very important mean of communication between people. But such networks are also used for malicious information dissemination and targeted influence on public opinion. The paper analyzes the characteristics of information dissemination in social networks. The contribution of the paper is the set of features that allow using machine learning methods to detect targeted influence on public opinion in social networks and distinguish the profiles responsible for it. The proposed features include: the dynamics of the number of subscribers, the dynamics of the number of likes on posts, the dynamics of the number of commentators on posts, the coherence of likes, the coherence of commentators, the dates of user registrations. The peculiarity of the proposed features lies in the selection of only those features that do not require content analysis. Also the paper authors propose the classification of the subjects that influence on the public opinion in social networks. The experiments were performed and proposed features and classification were tested.

[1]  Igor V. Kotenko,et al.  The Common Approach to Determination of the Destructive Information Impacts and Negative Personal Tendencies of Young Generation Using the Neural Network Methods for the Internet Content Processing , 2019, IDC.

[2]  Dan Mercea,et al.  The Brexit Botnet and User-Generated Hyperpartisan News , 2017 .

[3]  Julia Brennecke,et al.  The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study , 2017 .

[4]  James Morris.  Simulacra in the Age of Social Media: Baudrillard as the Prophet of Fake News , 2020 .

[5]  Lidia Vitkova,et al.  Approach to Identification and Analysis of Information Sources in Social Networks , 2019, IDC.

[6]  Mucahid Kutlu,et al.  Are We Secure from Bots? Investigating Vulnerabilities of Botometer , 2020, 2020 5th International Conference on Computer Science and Engineering (UBMK).

[7]  Andrey Chechulin,et al.  Hybrid Approach for Bots Detection in Social Networks Based on Topological, Textual and Statistical Features , 2019 .

[8]  Adrian Rauchfleisch,et al.  The False positive problem of automatic bot detection in social science research , 2020, PloS one.

[9]  D. Lazer,et al.  Fake news on Twitter during the 2016 U.S. presidential election , 2019, Science.

[10]  Victoria Vziatysheva.  How Fake News Spreads Online? , 2020, International Journal of Media and Information Literacy.

[11]  Guanhua Yan,et al.  The Rise of Social Botnets: Attacks and Countermeasures , 2016, IEEE Transactions on Dependable and Secure Computing.

[12]  Emilio Ferrara,et al.  Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election , 2017, First Monday.

[13]  Emilio Ferrara,et al.  Social Bots Distort the 2016 US Presidential Election Online Discussion , 2016, First Monday.

[14]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[15]  Emilio Ferrara,et al.  Bots, elections, and social media: a brief overview , 2019, Lecture Notes in Social Networks.

[16]  S. Bradshaw,et al.  Troops, Trolls and Troublemakers: A Global Inventory of Organized Social Media Manipulation , 2017 .

[17]  Tobias R. Keller,et al.  Social Bots in Election Campaigns: Theoretical, Empirical, and Methodological Implications , 2018, Political Communication.

[18]  Martin Grandjean.  Analisi e visualizzazioni delle reti in storia. L'esempio della cooperazione intellettuale della Società delle Nazioni , 2017 .

[19]  Social networks bot detection using Benford’s law , 2020, SIN.

[20]  Jay Lee,et al.  Simulating the spatial diffusion of memes on social media networks , 2019, Int. J. Geogr. Inf. Sci..