The Role of Adaptive Information Systems in Detecting Disruptive Communication Signals

Abstract

This paper analyzes the role of adaptive information systems in the early identification of potentially disruptive communication signals, in a context marked by information instability, digital acceleration and media pressure on institutions. Faced with the intensification of phenomena such as ideological polarization, the dissemination of disinformation and the emergence of radicalizing discourses, the ability of organizations to anticipate and interpret deviations from communication norms becomes essential, in order to protect decisional coherence and public trust. The paper is based on recent contributions from the fields of semantic analysis, natural language processing and machine learning, offering an integrated approach on how digital infrastructures can support contextual analysis of messages and the detection of weak signals. Adaptive information systems are described as complex algorithmic entities, capable of processing large volumes of unstructured data, identifying discursive patterns and generating, in real time, information risk scores. By integrating artificial intelligence and semantic analysis technologies, these systems support rapid, contextually relevant and anticipatory institutional responses. The paper thus proposes a conceptual framework applicable to the development of proactive mechanisms for discursive surveillance and communication instability management, with strategic relevance for educational, administrative and governmental environments.