POSSIBLE OPTIONS FOR IMPROVING EXISTING APPROACHES TO FAKE NEWS DETECTION BASED ON USING THE POTENTIAL OF MACHINE AND DEEP LEARNING ALGORITHMS, SENTIMENT OF NEWS CONTENT AND EMOTIONS IN USER COMMENTS
DOI:
https://doi.org/10.17721/2519-481X/2023/80-11Keywords:
online social networks, fake news, method, model, algorithm, formalizationAbstract
Currently, the Internet ranks first among sources of information. In the recent period, the role of online social networks (OSN) has significantly increased, which has both positive and negative consequences. The negative role of OSN is related to the spread of fake news that affects people's daily lives, manipulates their thoughts and feelings, changes their beliefs and can lead to wrong decisions. The problem of spreading fake news in OSN is currently global, and the formation of countermeasures is an urgent task today.
Today, there are various proven approaches to detecting fake news. In particular, one of the approaches is based on the use of different machine (ML) and deep (DL) learning algorithms. The other is based on the results of sentiment analysis of news content and analysis of emotions in user comments. The research conducted by the authors of other approaches to detecting fake news, which differ from the ones given, made it possible to conclude that the mentioned approaches are effective and promising in terms of using their potential for the development of new models with high performance indicators on various data sets.
In the article, the author's ideas regarding the improvement of existing approaches to detecting fake news based on the use of the potential of these approaches are formed and formalized. The first idea is based on the implementation of the mechanism of combining machine (ML) and deep (DL) learning methods, as well as the results of the analysis of the sentiment of news content and emotions in user comments, which takes into account the possibility of ensuring a sufficient level of effectiveness in detecting fake news, a certain level of the values of the selected metrics, as well as a certain level of functional characteristics of the author's method. The second idea is based on the implementation of a mechanism combining the functionality of two methods from among the specified two groups, which would provide optimal parameters for detecting fake news according to defined criteria and indicators.
The substantiation of the ideas involved the preliminary implementation of: setting the researched problem; functional analysis of machine (ML) and deep (DL) learning algorithms, as well as fake news detection algorithms based on the use of the results of sentiment analysis of news content and emotions in user comments; description of metrics for evaluating the effectiveness of methods for detecting fake news. According to the results of the substantiation of the perspective of the ideas, the tasks of detecting fake news in the author's production were formalized.
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