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.
Polina Verdieva (2025) studied this question.