Digital and Social Media Monitoring Agentic System for Brand Reputation
Year: 2025
Type: article
Abstract: Brand reputation is increasingly shaped by information that appears rapidly across news outlets, blogs, and social media platforms. Detecting, evaluating, and responding to reputational threats in real time is a daunting task when relying on manual monitoring, due to the volume and velocity of digital content. We present a Digital and Social Media Monitoring Agentic System (DSMAS) that integrates domain expert guidance with advanced artificial intelligence techniques to automate the surveillance and response cycle for brand reputation. The architecture is modular, featuring independent “agents” that specialize in distinct tasks such as sentiment analysis, contextual verification, and escalation decision making. Each agent is supervised by a human domain expert, ensuring that the system’s outputs remain reliable and that it mitigates the risk of hallucinations that can arise from generic language model behavior. The system’s data input layer incorporates a real time Integration Hub that aggregates feeds from multiple public APIs, while a Query Engine enables semantic enrichment of the raw data. Continuous feedback from experts is captured through a closed loop learning module, enabling agents to refine their models over successive iterations. An Action Engine translates high confidence detections into concrete responses such as automated public relations statements or alert triggers to a brand’s crisis management team. Experimental results demonstrate that DSMAS achieves 95 % coverage of relevant incidents, 92 % threat detection accuracy, and resolves 97 % of identified crises within the predefined service level agreement. The paper discusses the system’s design, implementation, evaluation, and its implications for the future of automated brand reputation management.
Authors: AHMAD, MUHAMMAD ZAKI, ABDUL MOIZ, MOHAMMED
Institution: Saudi Aramco (Saudi Arabia)
Language: English
Subfield: Communication
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7115006000
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