Detecting Sentiment Bias in Digital Media and Its Effects on User Belief Formation - Elliot Nolan; Aris T. Vance (Computational Intelligence Systems, 2026)
The proliferation of algorithmic news dissemination and the consolidation of digital media platforms have introduced unprecedented systemic risks to the process of human belief formation. Central to these risks is the phenomenon of sentiment bias—the systematic prevalence of specific emotional cues within information streams that can distort collective perception and individual decision-making. This research paper provides an extensive systems-level analysis of the mechanisms for detecting sentiment bias in digital media and evaluates its downstream effects on user belief formation. By treating the digital media landscape as a large-scale socio-technical infrastructure, we examine the structural trade-offs involved in deploying automated detection systems, the architectural requirements for robust sentiment monitoring, and the governance challenges inherent in managing algorithmic bias. We argue that the systematic prevalence of emotional cues is not merely a byproduct of journalistic style but often a structural feature of engagement-driven platforms. The study investigates the interplay between natural language processing methodologies and cognitive heuristics, emphasizing the need for fairness and transparency. Furthermore, we discuss the implications for democratic robustness and the sustainability of information ecosystems. By analyzing case illustrations from public health and financial markets, the paper provides a roadmap for the ethical integration of bias detection technologies into digital governance.