CLASSIFICATION OF FAKE NEWS IN UKRAINE AND ABROAD

M. Kitsa

Abstract


The aim of the work is to propose a broad classification of fake news based on the generalization of Ukrainian and international research.

Research methodology. Both theoretical and empirical research methods were used in the research process. The research methodology consisted of several stages. The first is data collection. This method was used to build a dataset of fake news articles from various sources. These sources included known purveyors of fake news, such as clickbait sites or biased blogs, as well as reputable news sources that have published fake news. The next stage was extraction of fake news features. After collecting a dataset of desinformation materials, we extract relevant functions that can be used as keywords for searching in Google. These data include word frequencies, grammatical structures, or other linguistic features that are known to be associated with fake news.

Results. Western researchers distinguish ten types of «fake news» [7]. Each of the ten forms of deceptive or illusory content carries a different level of threat, impact, and intent. The focus should be on identifying the types of content that are malicious and pose a threat of panic and confusion. Foreign researchers distinguish the following types of fakes: fake news, manipulation, deep fakes, puppet news, phishing, spreading rumors, bots, disinformation, clickbait, satire and parody. The above classification is quite narrow, as it covers specific examples of fake media publications. Considering that the media market and the Internet as a platform are dynamic, changing and reacting to external factors, a broader classification was proposed that would work in the longer term and that would also be able to adapt to dynamic changes in the genre.

Novelty. The novelty of this work is the proposed broad classification of fake news in media outlets on the basis of theoretical and empirical research.

Practical meaning. The obtained information can be used in further monitoring and research of fake news in Ukrainian and international media outlets. By accurately classifying fake news, the audience and journalists can identify the sources of misinformation and track the spread of false information. By developing different tools to classify fake news, other researchers can help educate the public on how to spot false information online and avoid being misled, which is an important aspect of media literacy.

Key words: fake news, disinformation, media, audience, clickbait.


References


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DOI: http://dx.doi.org/10.32840/cpu2219-8741/2023.1(53).7

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