Disclosure Standards for Social Media and Generative Artificial Intelligence Research: Toward Transparency and Replicability.
Disclosure Standards for Social Media and Generative Artificial Intelligence Research: Toward Transparency and Replicability.
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DOI：
10.1177/20563051231216947
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发表时间：
2023-10
影响因子：
5.2
通讯作者：
Emery, Sherry
中科院分区：
文献类型：
--
作者：
Kostygina, Ganna;Kim, Yoonsang;Seeskin, Zachary;Leclere, Felicia;Emery, Sherry
社交媒体主导着当今的信息生态系统，为社会研究提供了有价值的信息。市场研究人员、社会科学家、政策制定者、政府实体、公共卫生研究人员和从业人员认识到社会数据在激发创新、支持产品和服务、表征公众舆论和指导决策方面的潜力。挖掘这些丰富数据集的吸引力是显而易见的。然而，数据滥用的潜在风险凸显了该研究中一个同样巨大而根本的缺陷：没有程序标准，透明度也很低。由于专有算法，收集和分析社交媒体数据的整个过程的透明度往往受到限制。人工智能（AI）带来的虚假发现和偏见表明，缺乏透明度给研究带来了挑战。社交媒体研究仍然是一个虚拟的“狂野西部”，在数据检索、预处理步骤、分析方法或解释方面没有明确的报告标准。使用新兴的生成式人工智能技术来增强社交媒体分析可能会破坏研究结果的有效性和可复制性，可能会将这项研究变成一个“黑箱”企业。社会媒体分析和报道需要明确的指导，以确保研究结果的质量。在本文中，我们提出了基于既定科学实践的评估使用社交媒体数据的研究质量的标准。我们提供明确的文档指南，以确保社交数据在研究和应用中得到正确和透明的使用。建议列出一份符合最低报告标准的披露要素清单。这些标准将使学者和实践者能够使用数字数据评估研究结果的质量、可信度和可比性。
Social media dominate today’s information ecosystem and provide valuable information for social research. Market researchers, social scientists, policymakers, government entities, public health researchers, and practitioners recognize the potential for social data to inspire innovation, support products and services, characterize public opinion, and guide decisions. The appeal of mining these rich datasets is clear. However, there is potential risk of data misuse, underscoring an equally huge and fundamental flaw in the research: there are no procedural standards and little transparency. Transparency across the processes of collecting and analyzing social media data is often limited due to proprietary algorithms. Spurious findings and biases introduced by artificial intelligence (AI) demonstrate the challenges this lack of transparency poses for research. Social media research remains a virtual “wild west,” with no clear standards for reporting regarding data retrieval, preprocessing steps, analytic methods, or interpretation. Use of emerging generative AI technologies to augment social media analytics can undermine validity and replicability of findings, potentially turning this research into a “black box” enterprise. Clear guidance for social media analyses and reporting is needed to assure the quality of the resulting research. In this article, we propose criteria for evaluating the quality of studies using social media data, grounded in established scientific practice. We offer clear documentation guidelines to ensure that social data are used properly and transparently in research and applications. A checklist of disclosure elements to meet minimal reporting standards is proposed. These criteria will make it possible for scholars and practitioners to assess the quality, credibility, and comparability of research findings using digital data.
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影响因子：
4.2
作者：
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通讯作者：
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作者：
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影响因子：
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影响因子：
5.2
作者：
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通讯作者：
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DOI：
10.1111/jcom.12083
发表时间：
2014-04
期刊：
The Journal of communication
影响因子：
--
作者：
Emery SL;Szczypka G;Abril EP;Kim Y;Vera L
通讯作者：
Vera L