How the Algorithmic Transparency of Search Engines Influences Health Anxiety: The Mediating Effects of Trust in Online Health Information Search

Research Background and Problem

• Problem and Challenges: Search engines, particularly AI-driven search algorithms, enhance efficiency by directly answering health-related queries through "Featured Snippets" (FS). However, this may exacerbate users' health anxiety, especially when results contain alarming information. Trust in search results and over-reliance on FS can lead to misunderstandings of health information or inappropriate anxiety.
• Significance: Health anxiety has been widely recognized as a major negative effect of online health information search (OHIS). Searching behaviors can lead to "cyberchondria," impacting individuals' psychological well-being and healthcare utilization. Understanding and mitigating this effect is crucial for improving public health and search experiences.
• Research Motivation and Related Work:
  1. Literature highlights that trust is a key factor in OHIS, but excessive or misplaced trust can intensify anxiety caused by health information.
  2. FS in search engines is often perceived as a credible source of information, yet the fundamental differences between algorithmic ranking and professional medical diagnosis are frequently overlooked.
  3. The authors aim to explore whether algorithm transparency (AT) can serve as a strategy to address users' over-trust and health anxiety.

Solution

• Proposed Approach: The authors propose using "Algorithm Transparency Explanations" (AT Explanations) as an intervention to demonstrate the selection and ranking process of FS to users, thereby reducing over-trust in FS and search engines, as well as alleviating the resulting health anxiety.
• Innovations:
  1. Applying the theory of algorithm transparency to the study of the relationship between search engines and health anxiety.
  2. Experimentally validating the effect of AT on alleviating health anxiety and exploring its mechanism of influencing users' psychological states through the mediating role of trust.
  3. Proposing a novel method to calibrate users' trust in search engines, thereby reducing anxiety caused by information misinterpretation.
• Implementation Steps and Key Techniques:
  1. Experimental Design: Compare user behavior and psychological states under conditions with and without AT explanations in an online experiment.
  2. Experimental Procedure: Create simulated search scenarios where participants query "causes of drowsiness and poor sleep quality" and observe their reactions to FS content (including alarming information such as "narcolepsy").
  3. Data Collection and Analysis: Record the direct and indirect effects of AT explanations on trust in search engines, trust in information, and health anxiety. Use sequential mediation and moderation effect analysis to test hypotheses.

Research Findings

• Specific Findings:
  1. AT explanations significantly reduced users' trust in search engines (M=3.57 vs M=3.80) and trust in information (M=3.55 vs M=3.74).
  2. AT explanations indirectly alleviated health anxiety by gradually reducing trust in search engines and information, confirming the mediating role of trust.
  3. For users with a certain degree of cyberchondria, the negative effect of AT explanations on trust in health information was mitigated.
• Advantages:
  1. AT fosters critical thinking about search algorithms, helping users distinguish between algorithm-generated information and professional medical diagnoses.
  2. Provides a new method for reducing health anxiety, offering theoretical support for improving user experience in search engine design.
• Experimental or Evaluation Results:
  • AT explanations significantly increased participants' perceived transparency of algorithms (M=4.00 vs M=3.62), indicating the success of the experimental design.
  • Sequential mediation analysis showed that AT explanations significantly reduced health anxiety through decreased trust.
  • While the impact on users with severe cyberchondria was limited, the positive effects on general users remained significant.
• Limitations and Future Directions:
  1. Limitations:
     • The experimental environment was artificial and may not fully reflect the complexity of real-world OHIS.
     • Results focused on specific information (narcolepsy) may not generalize to other health information scenarios.
     • Did not directly measure participants' machine heuristic cognition (e.g., "Is the algorithm suitable for providing health decisions?") and its influence on outcomes.
  2. Future Directions:
     • Validate the effect of AT explanations on alleviating health anxiety in real-world settings.
     • Explore the impact of different types and styles of transparency explanations on trust and health anxiety.
     • Develop tools to measure users' machine heuristic cognition and examine its role in trust and emotional responses.

This study highlights the potential of algorithm transparency in alleviating anxiety during health information searches, while emphasizing the need for further validation in real-world and diverse scenarios.