一个大规模的AI相关推文数据集:结构和描述性统计
Nathalie de Marcellis-Warin1, Daniel Kouloukoui2,3, Thierry Warin4
1Polytechnique Montréal, Full Professor at Department of mathematics and industrial engineering, 2900 Edouard-Montpetit Boul, Montreal, Quebec, H3T1J4, Canada.
Data in brief
|August 21, 2025
概括
这项研究介绍了从2017年到2021年收集的893,000多条关于人工智能的匿名数据集. 这些数据支持人工智能研究
科学领域:
- 计算社会科学
- 自然语言处理
- 人工智能治理
背景情况:
- 人工智能 (AI) 的公共话语正在迅速发展.
- 要了解人工智能的社会影响,
- 现有的数据集可能缺乏全面的元数据或广泛的时间覆盖.
研究的目的:
- 提供有关人工智能的匿名数据集.
- 促进人工智能话语,公众感知和社会动态的研究.
- 为计算社会科学和NLP提供符合FAIR的资源.
主要方法:
- 使用Twitter API收集了893,076条推文 (2017年1月至2021年7月).
- 通过关键字"人工智能"进行过.
- 包括结构化元数据:参与度指标,媒体,标签,语言和时间.
主要成果:
- 人工智能相关推文的全面数据已公开.
- 数据集包括各种分析方法的丰富元数据.
- 数据符合FAIR的透明度和可复制性原则.
结论:
- 这一数据集是分析人工智能公共话语的宝贵资源.
- 能够在情绪分析和人工智能政策等领域进行跨学科研究.
- 促进计算社会科学和NLP的可重复性研究.
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