人工智能产生的内容标签对感知的准确性,信息可信度和共享误导信息的意图的影响:基于Web的,随机的,受控的实验
1School of Journalism and Communication, Beijing Normal University, Beijing, China.
JMIR formative research
|December 24, 2024
概括
人工智能产生的内容 (AIGC) 标签对感知到的准确性和可信度的影响最小,但有助于区分AIGC. 有效的AIGC标签需要细微的策略,特别是针对错误信息.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 数字媒体研究 数字媒体研究
背景情况:
- 生成型人工智能和人工智能生成的内容 (AIGC) 在虚拟平台上越来越普遍.
- 社交媒体平台开始标记AIGC,但对其影响的研究有限.
研究的目的:
- 调查AIGC标签对感知的准确性,信息可信度和共享错误信息的意图的影响.
- 完善AIGC标签在数字环境中的战略应用.
主要方法:
- 采用2x2x2混合实验设计,以AIGC标签 (存在/缺席),信息类型 (准确/不准确) 和内容类别 (营利/非营利) 为因素.
- 实验组和对照组的400名参与者评估了内容,并提供了关于准确性,可信度和分享意图的反.
- 统计分析包括重复测量ANOVA和简单效应分析.
主要成果:
- AIGC标签没有显著影响感知到的准确性,消息的可信性或分享意图.
- 信息类型和内容类别对所有三个依赖变量都有重大影响.
- 在信息类型和内容类别之间发现了重要的相互作用效应,以准确性和可信性,以及在信息类型和AIGC标签之间,以分享意图.
结论:
- AIGC标签对用户对准确性和可信度的看法影响很小,但有助于区分AIGC与人类内容.
- 标签不会对用户对平台内容的看法产生负面影响,这表明事实核查和治理的潜力.
- 需要细微的AIGC标签策略,根据信息类型而有所不同,特别是在错误信息方面,标签可能会略微提高共享意图和感知的准确性.
关键词:
在AIGC的标签上.聊天GPT 聊天 在GPT 聊天人工智能的人工智能是人工智能.生成型的人工智能卫生信息 卫生信息信息的可信性 信息的可信性错误的信息 错误的信息感知到的准确性分享意图分享的意图.社交媒体 社交媒体更多相关视频
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