(untitled) (Journal of Advanced Artificial Intelligence Research, 2026)
The proliferation of digital media platforms has fundamentally altered the landscape of public discourse, yet the mechanisms by which sentiment bias emerges, propagates, and shapes collective perception remain poorly understood from a systems perspective. This paper presents an interdisciplinary framework for detecting sentiment bias in digital media ecosystems and analyzing its downstream influence on large-scale belief formation. We conceptualize sentiment bias not merely as a statistical imbalance in positive or negative expressions, but as a structural property of socio-technical systems that arises from the interplay of algorithmic curation, platform governance, user behavior, and content production incentives. A detection methodology is proposed that integrates natural language processing pipelines with network analysis and temporal dynamics to identify biased sentiment distributions across platforms, topics, and demographic segments. The paper then examines how such biases interact with cognitive heuristics and social influence mechanisms to distort collective perception, leading to phenomena such as polarization, misperception of consensus, and the amplification of extreme views. System-level trade-offs are discussed in terms of computational scalability, fairness constraints, robustness to adversarial manipulation, and the sustainability of detection infrastructures. Finally, we explore governance and policy implications, including the design of bias-aware recommendation architectures, transparency requirements for algorithmic systems, and the ethical responsibilities of platform operators. The analysis draws on case studies from political discourse, public health communication, and consumer opinion ecosystems to illustrate the real-world consequences of undetected sentiment bias. By framing sentiment bias as a systemic challenge that spans engineering, social science, and regulatory domains, this paper contributes a unified vocabulary and analytical lens for researchers, practitioners, and policymakers seeking to mitigate the distorting effects of biased digital media on collective perception.