An information-analytical system for assessing the level of automated news content according to the population structure – A platform for media literacy system development

Beata Gavurova, Marinko Skare, Nik Hynek, Vaclav Moravec and Volodymyr Polishchuk

Technological Forecasting and Social Change, 2024, vol. 200, issue C

Abstract: An increasingly complicated communications environment, including artificially manufactured information, requires new skills. No research has designed a mechanism to assess public views and attitudes toward automated news material in this changing environment. This study used a fuzzy information–analytical system to assess public perceptions and attitudes toward automated news content as a tool for building a national and international media literacy system. Our two-stage innovative information approach uses three social indicators and eight criteria to assess public views toward artificially manufactured content. We defined the input–data processing system first. In the second stage, we used a multiple-criteria fuzzy model to assess public opinion on artificially manufactured information. We modified a generalized logical conclusion to varied query answers. For multiple criteria evaluation, we employed matrix multiplication and multidimensional membership functions to determine public opinion toward artificially manufactured content for different social indicator groups. Aggregated ratings and public attitude toward artificially manufactured content were the output. Experimental data from 1041 Czech respondents supported the information–analytical system's efficacy. With this knowledge, national and institutional decision-makers and other stakeholders can analyze citizens of different social and economic backgrounds' public attitudes toward artificially created content and create information security-related decision-making support scenarios.

Keywords: Artificially created content; Media literacy; Public attitude; Information security; Multiple criteria evaluation; Expert data; Fuzzy logical conclusion (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162523008466
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523008466

DOI: 10.1016/j.techfore.2023.123161

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().