社交媒体和生成人工智能研究的披露标准:朝着透明度和可复制性迈进
Ganna Kostygina1, Yoonsang Kim1, Zachary Seeskin1
1NORC at the University of Chicago, USA.
概括
社交媒体研究缺乏标准,造成滥用和偏见的风险. 本研究提出了透明的数据收集和分析的明确标准和指导方针,以确保研究质量和可信度.
科学领域:
- 社会科学 社会科学 社会科学
- 信息科学 信息科学 信息科学
- 数据科学数据科学数据科学
背景情况:
- 社交媒体平台是研究的主要信息来源.
- 在社交媒体数据分析中缺乏程序标准和透明度会带来风险.
- 专有算法和人工智能 (AI) 引入了偏差并限制了可复制性.
研究的目的:
- 为了解决社交媒体研究的"狂野西部"性质.
- 提出评估社交媒体数据研究质量的标准.
- 为研究中透明和适当使用社会数据提供指导方针.
主要方法:
- 基于已确立的科学实践.
- 建议评估研究质量的标准.
- 制定了明确的文件和披露准则.
主要成果:
- 建立了评估社交媒体研究质量的标准.
- 提供了透明度的文件准则.
- 提出了最低报告标准的检查清单.
结论:
- 实施拟议的标准提高了社交媒体研究的质量,可信度和可比性.
- 数据收集和分析的透明度对于可靠的调查结果至关重要.
- 标准化报告将减轻与社交媒体数据分析相关的风险.
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