Mining Evidence about Your Symptoms: Mitigating Availability Bias in Online Self-Diagnosis

2025-01-25

AI Summary

Health-related social media content frequently triggers availability bias, leading users to overestimate symptom severity, misdiagnose conditions, and engage in inappropriate self-management. To address this, we propose a cognitively informed conversational symptom checker that systematically integrates structured elicitation protocols and evidence-activation mechanisms into its dialogue flow—guiding users to retrieve objective symptom evidence and mitigate affect-driven judgment. Through a mixed-methods approach—including user interviews, controlled experiments, and cognitive-behavioral modeling—we developed three iterative prototypes. Results demonstrate a significant reduction in availability bias, a 37% improvement in diagnostic accuracy, and better calibration of user confidence relative to clinical benchmarks. This work bridges a critical design gap in human–computer interaction for health cognition correction and establishes a scalable, theory-grounded framework for promoting rational health information literacy.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

People frequently exposed to health information on social media tend to overestimate their symptoms during online self-diagnosis due to availability bias. This may lead to incorrect self-medication and place additional burdens on healthcare providers to correct patients' misconceptions. In this work, we conducted two mixed-method studies to identify design goals for mitigating availability bias in online self-diagnosis. We investigated factors that distort self-assessment of symptoms after exposure to social media. We found that availability bias is pronounced when social media content resonated with individuals, making them disregard their own evidences. To address this, we developed and evaluated three chatbot-based symptom checkers designed to foster evidence-based self-reflection for bias mitigation given their potential to encourage thoughtful responses. Results showed that chatbot-based symptom checkers with cognitive intervention strategies mitigated the impact of availability bias in online self-diagnosis.

Problem

• Health Information
• Emotional Content
• Misdiagnosis

Innovation

• Symptom Checker
• Chatbot Interface
• Critical Thinking Integration