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<p>This study investigates how stereotypes in online medical crowdfunding campaigns (OMCCs) influence donation intentions. We used both manual and LLM-based methods to extract visual, textual, and campaign features. Two crowdsourcing tasks were conducted: one involving human ratings from 150 participants and the other utilizing an LLM-simulated process. Results showed that campaigns with cover images conveying warmth and competence performed better. Both human and LLM judgments highlighted visual features as critical in predicting these perceptions, with key predictive features including hospital setting, contrasts between vitality and illness, number of images, and facial expressions, particularly smiles. Mediation analysis revealed that smiles indirectly influenced donations via warmth and competence. Images of hospital settings increased donations, while images of healthy subjects had a negative effect. This study highlights the impact of visuals in OMCCs and provides insight into how stereotypes can shape donation behavior. In addition, using LLMs to simulate crowdsourcing tasks overcomes the scale problem and enables efficient analysis of large datasets.</p>

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The influence of stereotypes and visual features on donation intentions in online medical crowdfunding campaigns: A comparison of survey and large language model-based methods

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月4日

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  • Xupin Zhang,
  • Xiaorong Zheng,
  • Jiebo Luo

摘要

This study investigates how stereotypes in online medical crowdfunding campaigns (OMCCs) influence donation intentions. We used both manual and LLM-based methods to extract visual, textual, and campaign features. Two crowdsourcing tasks were conducted: one involving human ratings from 150 participants and the other utilizing an LLM-simulated process. Results showed that campaigns with cover images conveying warmth and competence performed better. Both human and LLM judgments highlighted visual features as critical in predicting these perceptions, with key predictive features including hospital setting, contrasts between vitality and illness, number of images, and facial expressions, particularly smiles. Mediation analysis revealed that smiles indirectly influenced donations via warmth and competence. Images of hospital settings increased donations, while images of healthy subjects had a negative effect. This study highlights the impact of visuals in OMCCs and provides insight into how stereotypes can shape donation behavior. In addition, using LLMs to simulate crowdsourcing tasks overcomes the scale problem and enables efficient analysis of large datasets.