来自Meta和谷歌的可比的2022年大选广告数据集
Meiqing Zhang1, Furkan Cakmak1, Markus Neumann2
1Wesleyan University, Wesleyan Media Project, Middletown, 06459, USA.
Scientific data
|June 9, 2025
概括
本研究介绍了来自Meta和Google的2022年美国中期选举数字广告的数据集. 这些数据包括处理的视听和文本信息,用于分析政治竞选策略和公众参与.
科学领域:
- 政治科学 政治科学是指政治学.
- 沟通研究 沟通研究
- 数据科学数据科学数据科学
背景情况:
- 数字广告显著影响美国联邦选举.
- 了解在线政治运动需要可访问,全面的数据.
研究的目的:
- 在2022年美国中期选举期间,介绍两个新的数据集,详细介绍来自Meta和Google的数字广告.
- 为研究人员提供结构化数据,用于分析数字政治广告.
主要方法:
- 通过广告透明度库和从Meta (Facebook,Instagram) 和谷歌 (YouTube) 的网络抓取数据的数据收集.
- 处理包括自动语音识别 (ASR),面部识别和光学字符识别 (OCR).
- 通过标签和分类任务来丰富数据,以提高可比性和实用性.
主要成果:
- 创建两个关于美国联邦选举 (2022年中期) 数字广告的综合数据集.
- 数据集包含元数据,ASR转录,OCR文本,面部识别数据和分类标签.
- 经过处理的数据使广告内容和广告策略的详细分析成为可能.
结论:
- 这些数据集为分析数字选举广告格局提供了宝贵的资源.
- 促进政治科学,通信和数据科学方面的研究,以深入了解数字政治运动.
- 能够更深入地了解竞选策略和在线环境中的公众参与.
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