The modern digital ecosystem is characterized by an unprecedented volume of information and a lightning-fast speed of its dissemination, which renders the forecasting of trends in social networks the cornerstone of effective strategic communications. Within the scope of the study, a critical-systemic evaluation of methodological tools of artificial intelligence (AI) employed for predictive analytics in social media was conducted, followed by their adaptation to the tasks of public relations specialists. The aim of the research is to develop a conceptual framework that integrates various algorithmic AI approaches—from natural language processing (NLP) and computer vision to network analysis—for constructing proactive strategies in corporate reputation management and communication campaigns. The empirical foundation of the study is formed through a systematic review of leading scientific publications dedicated to the described technologies and their applied capabilities in the context of detecting and predicting public sentiment. The scientific novelty of the work lies in the formulation of a unified methodological paradigm that shifts the emphasis of PR activities from passive response to informational challenges to active shaping of media discourse. The conclusions obtained will be useful both to researchers in the fields of communications, data processing, and computational linguistics, and to practitioners — PR directors and heads of digital agencies.

artificial intelligence, trend forecasting, social networks, public relations

Meltwater; We Are Social. Digital 2024: Global Overview Report. [Electronic resource] Access mode: https://datareportal.com/reports/digital-2024-global-overview-report (date accessed: 17.06.2025).

Alshattnawi S. et al. Beyond word-based model embeddings: Contextualized representations for enhanced social media spam detection //Applied Sciences. – 2024. – Vol. 14 (6). – pp. 1-25. https://doi.org/10.3390/app14062254.

Pandey R. et al. Hybrid attention-based long short-term memory network for sarcasm identification //Applied Soft Computing. – 2021. – Vol. 106. https://doi.org/10.1016/j.asoc.2021.107348.

Musliadi K. H., Zainuddin H., Wabula Y. Twitter social media conversion topic trending analysis using latent Dirichlet allocation algorithm //Journal of Applied Engineering and Technological Science (JAETS). – 2022. – Vol. 4 (1). – pp. 390-399.

Chan J. Y. L. et al. State of the art: a review of sentiment analysis based on sequential transfer learning //Artificial Intelligence Review. – 2023. – Vol. 56 (1). – pp. 749-780.

Jin R., Liu X., Murata T. Predicting popularity trend in social media networks with multi-layer temporal graph neural networks //Complex & Intelligent Systems. – 2024. – Vol. 10 (4). – pp. 4713-4729.

Wang J. et al. Social media popularity prediction with multimodal hierarchical fusion model //Computer Speech & Language. – 2023. – Vol. 80. https://doi.org/10.1016/j.csl.2023.101490.

Giovanola B., Granata P. Ethics for human-centered education in the age of AI //Entrepreneurship and Digital Humanities. – Edward Elgar Publishing, 2024. – pp. 96-109.

Yue C. A. et al. Public relations meets artificial intelligence: Assessing utilization and outcomes //Journal of Public Relations Research. – 2024. – Vol. 36 (6). – pp. 513-534.

Gartner Predicts 75% of Analytics Content to Use GenAI for Enhanced Contextual Intelligence by 2027 . [Electronic resource] Access mode: https://www.gartner.com/en/newsroom/press-releases/2025-06-18-gartner-predicts-75-percent-of-analytics-content-to-use-genai-for-enhanced-contextual-intelligence-by-2027 (accessed: 06/25/2025).

Talkwalker. Social Media Trends Report 2024. [Electronic resource] Access mode: https://www.talkwalker.com/social-media-trends (date of access: 06/27/2025).

AI for Sales and Marketing Market worth $240.58 billion by 2030. [Electronic resource] Access mode: https://www.marketsandmarkets.com/PressReleases/ai-for-sales-and-marketing.asp (date accessed: 01.07.2025).