print

Presidential Campaign in Post-Truth Era:
Innovative Digital Technologies of Political Content Management in Social Networks Politics

Ryabchenko N.A.,

Associate Professor, Kuban State University, rrrnatali@mail.ru

elibrary_id: 637940 | ORCID: 0000-0001-6980-2894 | RESEARCHER_ID: Q-8079-2016

Malysheva O.P.,

Senior Lecturer, Kuban State University, malisheva_83@mail.ru

elibrary_id: 891265 | ORCID: 0000-0001-8285-0508 | RESEARCHER_ID: E-8066-2018

Gnedash A.A.,

Associate Professor, Kuban State University, anna_gnedash@inbox.ru

elibrary_id: 298533 | ORCID: 0000-0002-3516-107X | RESEARCHER_ID: S-1350-2016

DOI: 10.17976/jpps/2019.02.07

Rubric: Orbis terrarum

For citation:

Ryabchenko N.A., Malysheva O.P., Gnedash A.A. Presidential Campaign in Post-Truth Era: Innovative Digital Technologies of Political Content Management in Social Networks Politics. – Polis. Political Studies. 2019. No. 2. https://doi.org/10.17976/jpps/2019.02.07

Abstract

Modern socio-political discourse on various layers of communication exploits the notions of posttruth, post-truth politics and fake news, which became topical in the wake of the Brexit referendum and US Presidential Campaign of 2016. These events, as well as many others, caused a shift in political science and exposed a need for the reconsideration of the impact of online space on the development of socio-political systems all over the world. Besides, these events exposed a research need for political content studies in the online space at micro and macro-levels. The online processes are omplex and non-linear, thus, require complex and hybrid analytical methods – for example, a combination of network analysis, mathematic analysis and linguistic and discourse analysis – the tools used by the authors of the article to study the political content produced during the Trump’s US Presidential Campaign 2016. The purpose of the research was to identify the peculiarities of political content production and the impact it could have on prospective voters (such as shaping public opinion, agitation and construction of a required expected behavior). The empirical foundation of the research is metadata of the social networks, required for a network analysis; likewise, the text corpus, a set of messages produced by D. Trump’s team during the US Presidential Campaign 2016 in the same social networks, was the empirical base for linguistic discourse analysis. The network analysis, which originates from the theory of complex networks, was applied to study of the process of political content consumption by online users (prospective and active voter groups), and in particular, how the production capacity of social network users, as regards the produced or spread political content, can determine their roles (the opinion leaders, the sensors, the implementers, the readers, the reputation players). The linguistic discourse analysis allowed to identify the key peculiarities of the researched political content, produced as a part of a successful marketing campaign (aimed at promoting a candidate for the presidency) in social networks and online space as a whole. The study of the Trump’s US Political Campaign 2016 revealed that the online dominance, acquired via social network marketing (which is a result of communicative strategies exploitation), can convert into a political dominance in the offline space. 

Keywords

post-truth, post-truth politics, alternative facts, ‘filter bubble’, fake news, political content management, the Internet, social networks, types of users, linguistic discourse analysis, Donald J. Trump’s US Presidential Campaign.

 Full Text  

Albert R., Barabasi A.-L. 2002. Statistical Mechanics of Complex Networks. – Reviews of Modern Physics. Vol. 74. No. 2. Р. 47-97. https://doi.org/10.1103/RevModPhys.74.47

Barabasi A.-L. 2009. Scale-Free Networks: A Decade and Beyond. – Science. Vol. 325. No. 5939. P. 412-413. https://doi.org/10.1126/science.1173299

Batorski D., Grzywinska I. 2018. Three Dimensions of the Public Sphere on Facebook. – Information, Communication & Society. Vol. 21. No. 3. P. 356-374. https://doi.org/10.1080/1369118X.2017.1281329

Bhatia S., Goodwin G.P., Walasek L. 2018. Trait Associations for Hillary Clinton and Donald Trump in News Media. A Computational Analysis. – Social Psychological and Personality Science. Vol. 9. No. 2. P. 123-130. https://doi.org/10.1177/1948550617751584

Discourse and Digital Practices: Doing Discourse Analysis in the Digital Era. 2015. Ed. by R.H. Jones, A. Chik, C.A. Hafner. L.; N.Y.: Routledge. Enli G. 2017. Twitter as Arena for the Authentic Outsider: Exploring the Social Media Campaigns of

Trump and Clinton in the 2016 US Presidential Election. – European Journal of Communication. Vol. 32. No. 1. P. 50-61. https://doi.org/10.1177/0267323116682802

Kayam O. 2018. The Readability and Simplicity of Donald Trump’s Language. – Political Studies Review. Vol. 16. No. 1. P. 73-88. https://doi.org/10.1177/1478929917706844

Keneshloo Y., Cadena J., Korkmaz G., N. Ramakrishnan. 2014. Detecting and Forecasting Domestic Political Crises: A Graph-Based Approach. – Proceedings of the 2014 ACM Conference on Web Science. N.Y., USA, ACM. Р. 192-196. https://doi.org/10.1145/2615569.2615698

Pariser E. 2011. The Filter Bubble: What the Internet Is Hiding from You. N.Y.: Penguin Press. Pearson M. 2017. Teaching Media Law in a Post-truth Context: Strategies for Enhancing Learning about the Legal Risks of Fake News and Alternative Facts. – Asia Pacific Media Educator. Vol. 27. No. 1. P. 17-26. https://doi.org/10.1177/1326365X17704289

Ryabchenko N., Gnedash A. 2015. Structure, Types of Users and the Practices of Online-Social Networks as a Field of Political Practices. – International Review of Management and Marketing. Vol. 5. No. 1S. P. 115-120. URL: http://www.econjournals.com/index.php/irmm/article/view/1628/pdf (accessed 05.02.2019).

Smith D.N., Hanley E. 2018. The Anger Games: Who Voted for Donald Trump in the 2016 Election, and Why? – Critical Sociology. Vol. 44. No. 2. Р. 195-212. https://doi.org/10.1177/0896920517740615

Spohr D. 2017. Fake News and Ideological Polarization: Filter Bubbles and Selective Exposure on Social Media. – Business Information Review. Vol. 34. No. 3. P. 150-160. https://doi.org/10.1177/0266382117722446

Tracey S. 2017. Trust, Trump, and the Turnout: A Marketers Point of View. – American Behavioral Scientist. Vol. 61. No. 5. P. 526-532. https://doi.org/10.1177/0002764217701218

Arendt H. 2014. Between Past and Future: Eight Exercises in Political Thought. (Russ. ed. Arendt H. Mezhdu proshlym i budushhim. Vosem’ uprazhnenij v politicheskoj mysli. Moscow: Izdatelstvovo Instituta Gajdara. 416 p.)

Bykov I.A. 2017. Mediatization of Politics in the Era of Social Media. – Journal of Politic Research. Vol. 1. No. 4. P. 15-38. (In Russ.)

Chugrov S.V. 2017. Post-truth: Transformation of Political Reality or Self-Destruction of Liberal Democracy? – Polis. Political Studies. No. 2. P. 42-59. (In Russ.) https://doi.org/10.17976/jpps/2017.02.04

Danilova A.A. 2009. Manipulirovanie slovom v sredstvah massovoĭ informacii [Verbal Manipulation in Mass Media]. Moscow: Dobrosvet, KDU Press. 234 p. (In Russ.)

Gnedash A.A., Ryabchenko N.A. 2014. Constructive and Destructive Socio-Political Practices in Today Russia Online-Space: Fail, Case, Mechanics. – Human. Community. Management. No. 2. P. 40-54. (In Russ.)

Kochler H. 2013. The New Social Media: Chance or Challenge for Dialogue? – Polis. Political Studies. No. 4. P. 75-87. (In Russ.) URL: http://www.politstudies.ru/en/article/4734 (accessed 04.02.2019).

Leshhev S.V. 2002. Kommunikativnoe, sledovatel’no, kommunikacionnoe [Communicative, Therefore, Communicational]. Moscow: Editorial URSS. 172 p. (In Russ.)

Miroshnichenko I.V., Morozova E.V. 2017. Network Public Policy: Outlines of Subject Field. – Polis. Political Studies. No. 2. P. 82-102. (In Russ.) https://doi.org/10.17976/jpps/2017.02.05

Ryabchenko N.A. 2016. Toponymy of Network Landscape in Online-Space. – Human. Community. Management. No. 4. P. 41-53. (In Russ.)

Solovey V.D. 2017. Digital Mythology and Donald Trump Electoral Campaign. – Polis. Political Studies. No. 5. P. 122-132. (In Russ.) https://doi.org/10.17976/jpps/2017.05.09

Yevin I.A. 2010. Introduction to the Theory of Complex Networks. – Computer Research and Modeling. Vol. 2. P. 121-141. (In Russ.) URL: http://crm-en.ics.org.ru/uploads/crmissues/crm2010-2-2/crm10201.pdf (accessed 05.02.2019). 

See also: