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

TL;DR

This paper investigates how social media influences online self-diagnosis, identifies factors causing availability bias, and proposes chatbot-based tools with cognitive strategies to promote evidence-based symptom assessment and reduce bias.

Contribution

It introduces and evaluates chatbot-based symptom checkers with cognitive interventions to mitigate availability bias in online health self-diagnosis.

Findings

Chatbots with cognitive strategies reduce availability bias effects.

Availability bias is stronger when social media content resonates personally.

Design goals for bias mitigation in online self-diagnosis are identified.

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.