Sentiment-Aware Fake News Detection via Joint Semantic and Emotional Representation Learning
Keywords:
fake news detection, sentiment analysis, emotion representation, deep learning, socio-technical systems, information integrity, joint representation learningAbstract
The rapid diffusion of false information through digital platforms poses serious risks to democratic deliberation, public health, and institutional trust. Traditional fake news detection systems often rely on semantic representations alone, treating misinformation as a problem of factual divergence or stylistic abnormality. This paper argues that such a treatment is insufficient because fabricated content is frequently engineered to exploit emotional arousal, moral outrage, and identity-based sentiment. A system-level architecture is proposed in which semantic encoding and affective representation are learned jointly, allowing the detection pipeline to capture the interplay between linguistic meaning and emotional manipulation. The architecture integrates contextual language modeling, emotion-aware representation modules, supervised contrastive learning objectives, and a fusion layer that aligns semantic and affective signals before classification. Beyond algorithmic design, the paper examines structural trade-offs related to computational infrastructure, latency, model maintenance, adversarial resilience, fairness, and regulatory compliance. The discussion positions fake news detection not merely as a machine learning task but as a socio-technical infrastructure requiring governance, auditability, and sustainable deployment practices. The paper contributes a conceptual and architectural framework for sentiment-aware fake news detection while offering forward-looking perspectives on robustness, explainability, and institutional integration.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
