The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection

Misinformation remains one of the most significant issues in the digital age. While automated fact-checking has emerged as a viable solution, most current systems are limited to evaluating factual accuracy. However, the detrimental effect of misinformation transcends simple falsehoods; it takes advantage of how individuals perceive, interpret, and emotionally react to information. This underscores the need to move beyond factuality and adopt more humancentered detection frameworks. In this survey, we explore the evolving interplay between traditional fact-checking approaches and psychological concepts such as cognitive biases, social dynamics, and emotional responses. By analyzing state-of-the-art misinformation detection systems through the lens of human psychology and behavior, we reveal critical limitations of current methods and identify opportunities for improvement. Additionally, we outline future research directions aimed at creating more robust and adaptive frameworks, such as neurobehavioural models that integrate technological factors with the complexities of human cognition and social influence. These approaches offer promising pathways to more effectively detect and mitigate the societal harms of misinformation. Survey Paper Cognitive Framing Human-Centric Analysis Behavioral Insights Relevance to HC Guo et al. (2022) ✗ Not addressed ✗ No focus on human factors ✗ Absent • Low Hardalov et al. (2022) ✓ Discusses stance as a proxy for belief and agreement ✓ Explores how stance reflects user attitudes ▲ Mentions role in misinformation spread • Moderate Akhtar et al. (2023) ✓ Notes higher credibility of multimodal misinformation ▲ Highlights human susceptibility to images/videos ▲ Suggests need for user studies • Moderate Vladika and Matthes (2023) ✗ Focuses on technical aspects ✗ No discussion on user cognition ✗ Absent • Low Nakov et al. (2024) ▲ Touches on media bias perception ▲ Discusses impact on public trust ▲ Limited behavioral analysis • Moderate Panchendrarajan and Zubiaga (2024) ✗ Emphasizes linguistic challenges ✗ No cognitive aspects discussed ✗ Absent • Low Eldifrawi et al. (2024) ▲ Emphasizes the importance of explainability in fact-checking systems ▲ Discusses the need for user-understandable justifications ▲ Highlights the role of user trust in automated fact-checking • Moderate Fact-checking was conducted by journalists and editors at newspapers and magazines. Heuristics and social trust guided public belief, relying on trusted institutions. Fact-checking operated at a limited scale with less visibility than today's digital platforms. Before Internet Democratization Increased research in psychology to understand AI models' behavior. Integration of psychological principles into AI fact-checking systems.