Assessment of News Items Objectivity in Mass Media of Countries with Intelligence Systems: the Brexit Case

Assessment of News Items Objectivity in Mass Media of Countries with Intelligence Systems: the Brexit Case

TATYANA N. VLADIMIROVA1, Marina V. Vinogradova2,
ANDREY I. VLASOV3, & Alexander A. Shatsky4
1 Moscow Pedagogical State University, Russian Federation
2 Russian State Social University, Russian Federation
3 Bauman Moscow State Technical University, Russian Federation

Abstract

The role of mass media in society keeps the problem of manipulative influence distinction and the contiguous phenomena, chief among which is objectivity and authenticity of news items, current. The research provides a detailed study of the information broadcasting mechanisms in the media area, defines the problems, impeding an impersonal reproduction and disclosure of information, clarifies the verification methods, and gives their topology. In this research, we examined how the mass media of different countries presented the same event to the public. The publications of four mass media, concerning such an event as the withdrawal of the United Kingdom from the European Union (Brexit), have been determined as an object of the analysis. The chosen mass media refer to the countries, which are not the direct participants of that process: Russia, the USA, and Ukraine. D. Brewer’s criteria were used to define the objectivity of the news items. A relative sentiment of the news, which became the objective analysis basis, has been identified using linguistic rate with Eureka Engine intelligence system. The obtained results predominantly confirmed the hypothesis, that the mass media of different countries would represent the process of the UK withdrawal from the EU according to the country’s policy and interpret the facts in their favor. All the four mass media demonstrate the partiality when broadcasting the current situation in the matter of Brexit. The concepts being the semantic kernel elements of mass media publications have emotional coloring. The sentiment analysis of the publications resulted in the conclusion that only one of the four mass media gave a neutral assessment of the Brexit situation. The other three held to the political stance of their edition or government. The research results indicate that the problem of mass media objectivity remains relevant. The correctional impact on public opinion through mass media is extremely high. Therefore, forming the personal attitude toward the situation or event should occur with using several verifications methods and mass media sources at once.

Keywords: Content analysis, mass media, objectivity, manipulation, semantic kernel, information, sentiment analysis of news items, public opinion, intelligence systems

References

Allabouche, K., Diouri, O., Gaga, A., & El Amrani El Idrissi, N. (2016). Mobile phones’ social impacts on sustainable human development: case studies, Morocco, and Italy. Entrepreneurship and Sustainability Issues, 4(1), 64-73. https://doi.org/10.9770/jesi.2016.4.1(6)
Antonova, A., & Solovjev, A. (2013). Conditional random field models for the processing of Russian. Computational linguistics and intelligent technology, 2, 27-44. Moscow: RSUH.
Arutiunova, N.D. (1990). Metaphor and discourse. Theory of metaphor, 5-32.
Bessonov, B.N. (1971). Propaganda and manipulation as spiritual enslavement tools. Moscow: Mysl.
Blakar, R.M. (1987). Language as an instrument of social power. Moscow: Progress
Brewer, D. (2018). Media Helping Media. Retrieved from: https://www.mediahelpingmedia.org
Bykova, O.N. Linguistic manipulation: materials for the encyclopedic dictionary “Culture of Spoken Russian.” Theoretical and applied aspects of oral communication, 1(8), 91-103.
Chernova, V. Y., Tretyakova, O. V, & Vlasov, A. I. (2018). Brand marketing trends in Russian social media. Media Watch, 9(3), 397–409. https://doi.org/10.15655/mw/2018/v9i3/49478
Chetviorkin, I. I., & Loukachevitch, N. V. (2013). Sentiment analysis track at romip-2012. Computational linguistics and intelligent technology, 2, 40-50.
Danilin P.V. (2018). Propaganda and ideology. Manipulation. Topology [Lecture notes]. Retrieved from http://evartist.narod.ru/text28/0001.
Deryabina, A.S. (2016). Veracity, objectivity, and humanism in a journalist’s perception. Mediascope, 3. Retrieved from http://www.mediascope.ru/node/2133
Eureka Engine. Retrieved from http://eurekaengine.ru/ru/
Fokina, E.N., Nikitina, N.I., & Vinogradova, M.V. (2018). Citation of mass media resources in the social network. Media Watch, 9(3), 361-371. https://doi.org/10.15655/mw/2018/v9i3/49481
Giessen, H. W. (2015). Sustainable entrepreneurship and peculiarities of media-based learning. Entrepreneurship and Sustainability, 2(3), 154-162. https://doi.org/10.9770/jesi.2014.2.3(4)
Jdanova, E.V. (2010). Personality and communication: verbal communication tutorial. Moscow: Flinta: Nayka.
Kara-Murza, S.G. (2000). Mind control. Moscow” Algoritm.
Komarov, V.G. (1986). Fact, truth, and veracity in journalism. Jurnalist, Pressa, auditoria, 3, 88-89.
Kopnina, G.A. (2012). Linguistic manipulation. Moscow: Flinta.
Lakoff, D., & Jonson, M. (2004). The metaphors of our lives. Moscow: URSS.
Limba, T., & Sidlauskas, A. (2018). Peculiarities of anonymous comments’ management: a case study of Lithuanian news portals. Entrepreneurship and Sustainability, 5(4, 875-889. https://doi.org/10.9770/jesi.2018.5.4(12)
Liu, B. (2010). Sentiment analysis and subjectivity. In Indurkhya N, Damerau FJ (Eds.). Handbook of natural language processing (pp 627–666). FL: CRC Press, Taylor and Francis Group, Boca Raton.
Nikiforov, A.L. (2008). The concept of truth in socio-humanistic cognition. Russia, Moscow: Institute of Philosophy, Russian Academy of Sciences
Oganyan, V.A., Vinogradova, M.V., & Volkov, D.V. (2018).Internet piracy and vulnerability of digital content. European Research Studies Journal, 21(4), 735-743.
Pang, B., & Lee, L. (2002). Shivakumar Vaithyanathan Thumbs up? Sentiment Classification using Machine Learning Techniques. Proceedings of EMNLP, 79-86. https://doi.org/10.3115/1118693.1118704
Pang, B., & Lee, L. (2008) Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1), 1-135. https://doi.org/10.1561/1500000011
Prikhodko, A.I. (2015). The specific features of the argumentation in media. Belgorod State University Scientific Bulletin, 18(215), 24-30.
Scheible Ñ., & Schutze, H. (2013). Sentiment Relevance. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Sofia.
Scherbatyh, Y.V. (1998). Art of deception. Moscow: EKSMO-Press
Sedov, K.F. (2003). About manipulation and actualization in linguistic manipulation. Verbal communication issues, 2, 20-27.
Semina, T.A. (2018). Ò.À. Subjectivity vs. objectivity dichotomy and sentiment relevance in sentiment analysis tasks. Vestnik Moskovskogo gosudarstvennogo oblastnogo universiteta, 1, 38-45. https://doi.org/10.18384/2310-712X-2018-1-38-45
Sheinov V.P. (2008). Ñovert human control. Minsk: AST, Kharvest.
Shostrom, E.L. (2008). The manipulator: The inner journey from manipulation to actualization. Moscow: Aprel-Press.
Shragina, L.I. (2014). Metaphora as imagination phenomen. The Ukrainian psychological and educational edited volume, 2(2), 169-175.
Sisulak, S. (2017). User focus – tool for criminality control of social networks at both the local and international level. Entrepreneurship and Sustainability, 5(2), 297-314. https://doi.org/10.9770/jesi.2017.5.2(10)
Skovorodnikov, A.P. (2005). Rhetorical question. Encyclopedic dictionary-guide. Expressive means of the Russian language and faults of speech (Vol 1, pp. 266-270). Moscow: Flinta.
Strenin I.A. (2012). The basics of linguistic manipulation. Voronezh: Istoki.
Turney, P. (2002). Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. Proceedings of the Association for Computational Linguistics, 417-424. https://doi.org/10.3115/1073083.1073153
Tyrygina V.I. (2010). Genre stratification of mass media discourse. Moscow: LIBROKOM.
Yessenbekova, U. M. (2015). Role of media culture in national historical preservation. Social Sciences (Pakistan), 10(8), 2199–2205. http://doi.org/10.3923/sscience.2015.2199.2205
Yessenbekova, U. M. (2016). Promotion of national traditions by Kazakhstan mass media as a mean of ideological influence. Indian Journal of Science and Technology, 9(9), 35-42. http://doi.org/10.17485/ijst/2016/v9i9/86600
Yessenbekova, U. M. (2018a). Television in the development of information society culture in Kazakhstan. Media Watch, 9(3), 411-417. http://doi.org/10.15655/mw/2018/v9i3/49498
Yessenbekova, U. M. (2018b). Transformation of the functions of Kazakhstan television in the information society. Media Watch, 9(2), 203-208. http://doi.org/10.15655/mw/2018/v9i2/49387
Zimin, V.A. (2012). Role of Mass Media in Formation of Political Culture and Development of Institutes of the Civil Society in Russia. Izvestia Saratovskogo universiteta, 12(1), 91-94. Retrieved from https://soziopolit.sgu.ru/ru/articles/rol-smi-v-formirovanii-politicheskoy-kultury-i-razvitii-institutov-grazhdanskogo

Tatyana N. Vladimirova (Dr. Sci. of Pedagogic received at the Military University of the Ministry of Defense of the Russian Federation in 2015; Cand. Sci. Philological received at the Moscow State Open Pedagogical University named after M. Sholokhov in 2003) is the Professor and Director of the Institute of Journalism, Vice-Rector for Public Relations, Moscow Pedagogical State University (Moscow, Russian Federation). Her research interests are journalism, professional education, information technologies, modern educational technologies, innovations in the field of management, and psychological portrait of a person.

Marina V. Vinogradova (Dr.Sci. of Economic received at the Russian State University of Tourism and Service in 2013) is the Professor and Director of Research Institute of Advanced Directions and Technologies, Russian State Social University, Russian Federation. Her research interests are socio-economic development of macro and microsystems, socio-cultural problems, forecasting, information systems.

Andrey I. Vlasov (Cand.Sci. of Engineering received at the Bauman Moscow State Technical University in 1997), Assistant Professor, Bauman Moscow State Technical University. His research interests are a public-private partnership, information technology, Big data, Internet of things, investment management, marketing planning, intellectual analysis of social communications.

Alexander A. Shatsky an applicant for candidate degree, Russian State Social University, Russian Federation. His research interests are socio-economic development, multi-agent technologies, digital economy, management of economic systems, service management.