Using artificial intelligence in SN analysis: building models to understand and predict user behavior.

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dc.contributor.authorYarets’ka, N.O.
dc.contributor.authorBorovyk, L.V.
dc.contributor.authorTraskovetska, L.М.
dc.contributor.authorRamskyi, A.O.
dc.contributor.authorPoplavskaya, O.A.
dc.date.accessioned2024-11-08T09:59:45Z
dc.date.available2024-11-08T09:59:45Z
dc.date.issued2024-10-29
dc.descriptionhttps://revistagt.fpl.emnuvens.com.br/get/article/view/2879
dc.description.abstractThe study explores the use of artificial intelligence (AI) and sentiment analysis to predict personality traits and behaviors from the extensive data available on social networks. It aims to understand the dynamics of user interaction and the spread of viral content through AI-driven models. The research applies various AI and machine learning techniques, particularly focusing on natural language processing (NLP), to analyze social media data. The methodology includes sentiment analysis to categorize text into distinct emotional responses and predictive analytics to forecast trends in user engagement and content virality. Results indicate that AI can effectively predict user behaviors and personality traits such as neuroticism, which correlates with higher aggression and more frequent, prolonged use of social media. The study identifies key patterns and trends that influence user interactions on social networks. The discussion centers on the implications of AI in social media analytics, addressing both the technological advancements and the ethical considerations of profiling user behavior. It emphasizes the need for robust models that can handle the complexity and variability of data in social networks. The research demonstrates that AI and machine learning are invaluable tools for analyzing social networks, providing insights that can enhance user engagement strategies and content delivery. The study advocates for continued development and refinement of AI models to better understand and predict user behavior.
dc.description.sponsorshipYarets’ka, N. O., Ramskyi, A. O., Poplavskaya, O. A.
dc.identifier.citationYarets’ka, N. O., Borovyk, L. V., Traskovetska L. М., Ramskyi, A. O., & Poplavskaya, O. A. (2024). Using artificial intelligence in SN analysis: building models to understand and predict user behavior. Journal of Management & Technology, v. 24. p. 287–303.
dc.identifier.issn2177-6652
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/17017
dc.language.isoen
dc.publisherJournal of Management & Technology
dc.subjectbig data analysis
dc.subjectmachine learning in SNs
dc.subjectprediction of user activity
dc.subjectclassification algorithms
dc.subjectdetection of behavior patterns
dc.titleUsing artificial intelligence in SN analysis: building models to understand and predict user behavior.
dc.typeСтаття
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