Online Media Audience during the COVID-19 Pandemic as an Active Amplifier of Disinformation: Motivations of University Students to Share Information on Facebook

Author:

Tkáčová Hedviga1,Maturkanič Patrik2,Pavlíková Martina3,Slobodová Nováková Katarína4

Affiliation:

1. Faculty of Arts and Letters, Catholic University, Hrabovská cesta 1B, 034 01 Ružomberok, Slovak Republic

2. College of Applied Psychology, Akademická 409, 411 55 Terezin, Czech Republic

3. Faculty of Arts, Constantine The Philosopher University, Tr. A. Hlinku 1, 949 01 Nitra, Slovak Republic

4. Faculty of Arts, University of Ss.Cyril and Methodius, Nám. J. Herdu 2, 917 01 Trnava, Slovak Republic

Abstract

Disclosure of disinformation has attracted increasing attention in recent years. The society recognises that false reports pose a real threat to the credibility of information and, ultimately, to the security of society. On the Internet an active audience is a distributor of media content because they are convinced of its truth, and in the online environment they find it in other people. Therefore, the audience seems to be an active amplifier of disinformation (sharing), and thus explicitly as a creator of (unwanted) web content (sharing and commenting). People’s willingness to share disinformation is linked to people’s similar attitudes; it is related to the similarity of faith and to the perception of the message, considered as appropriate and interesting (“I like it”), etc. The term “homogeneity” turns out to be a key term in audience research, and experts speak about a phenomenon that in fact appears to be the main driving force for the dissemination of any content. The aim of the research is to identify and classify the factors that motivate university students to share information on the social networking site Facebook.

Publisher

University of Saints Cyril and Methodius

Subject

Marketing,Communication

Reference59 articles.

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2. BOBERG, S. et al.: Pandemic Populism: Facebook Pages of Alternative News Media and the Corona Crisis: A Computational Content Analysis. In Computer Science Journal, 2020, Vol. 1, p. 1-21. ISSN 1508-2806.

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