算法作为推来源和说服性的健康沟通:源线索的影响,语言强度和感知问题的参与
1School of Journalism and Information Communication, Huazhong University of Science and Technology.
Health communication
|August 1, 2023
概括
推在线健康信息的算法与其他来源一样有说服力. 然而,消息强度和用户参与显著影响算法的说服力,特别是与像CDC这样的可信来源相比.
科学领域:
- 卫生沟通健康沟通
- 人与计算机的交互
- 社会媒体研究 社交媒体研究
背景情况:
- 算法越来越多地影响在线健康信息的选择和建议.
- 通过算法推的内容来理解用户的说服力对于公共卫生至关重要.
- 之前的研究还没有完全探索算法与健康环境中的其他来源的比较说服力.
研究的目的:
- 调查何时以及为什么个人被算法推的健康信息所说服.
- 将算法的说服力效应与其他推来源 (其他用户,朋友,CDC) 进行比较.
- 检查语言强度的调节作用和对算法说服的参与问题.
主要方法:
- A 4 (推来源:算法,其他用户,朋友,CDC) x 2 (语言强度:高与低) 在受试者之间进行实验.
- 参与者 (N=299) 在社交媒体上接触到与健康相关的公共服务公告.
- 合规意图被衡量为主要结果.
主要成果:
- 总的来说,与其他推来源相比,算法诱导了类似的合规意图.
- 在推来源,语言强度和问题参与之间发现了显著的三向相互作用.
- 对于参与度低的人来说,来自算法的高强度信息的说服力不如CDC的说服力.
- 对于高度参与的个人来说,算法引起的恐惧比CDC更强烈的信息,增加了合规意图.
结论:
- 对健康信息的算法建议可以和传统来源一样有效.
- 算法的有效性取决于消息特征和用户参与度.
- 根据用户参与和消息强度量身定制算法建议是最大化公共卫生影响的关键.
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