Datasets for "From authority to similarity: how Google transformed its knowledge infrastructure using computer vision"

We investigate the impact of computer vision models, a prominent artificial intelligence tool, on critical knowledge infrastructure, using the case of Google search engines. We answer the following research question: How do search results for Google Images compare internationally with those for Google Search, and how can these results be explained by changes in Google’s knowledge infrastructure?<b> </b>To answer this question, we carry out four steps: 1) theorise the relationship between web epistemology, calculative technology, platform vernacular and issue configuration, illustrating the dynamics of critical knowledge infrastructures on the web; 2) provide a potted history of Google’s use of computer vision in search; 3)<b> </b>undertake<b> </b>the first international comparison of search results from Google Search with Google Images; 4) analyse the visual content of search results from Google Images. Using quanti-quali digital methods including visual content analysis, social semiotics and computer vision network analysis, we analyse search results related to environmental change across six countries, with two key findings. First, Google Images search results contain fewer authoritative sources than Google Search across all countries. Second, Google Images results constitute a narrow, homogenised visual repertoire across all countries. This constitutes a transformation in web epistemology from ranking-by-authority to ranking-by-similarity, driven by a shift in calculative technology from web links (Google Search) to computer vision (Google Images). Our findings and theoretical model open up new questions regarding the impact of computer vision on the public availability of knowledge in our increasingly image-saturated digital societies.

本研究以谷歌搜索引擎（Google）为案例，探究作为主流人工智能工具的计算机视觉模型（computer vision）对关键知识基础设施的影响，并解答如下研究问题：谷歌图片搜索（Google Images）与谷歌网页搜索（Google Search）的国际搜索结果存在何种差异？此类差异又可通过谷歌关键知识基础设施的更迭得到何种解释？
为解答该问题，本研究开展四项工作：1）构建网络认识论、计算性技术、平台通用话语与议题构型之间的理论关联，阐明网络空间关键知识基础设施的运行逻辑与动态演化；2）梳理谷歌在搜索服务中应用计算机视觉的简要发展历程；3）开展首次针对谷歌网页搜索与谷歌图片搜索结果的跨国对比分析；4）分析谷歌图片搜索结果的视觉内容。
本研究采用视觉内容分析、社会符号学、计算机视觉网络分析等定量与定性混合的数字研究方法，对六个国家的环境变化相关搜索结果展开分析，得到两项核心发现：其一，在所有调研国家中，谷歌图片搜索结果所包含的权威信源数量均少于谷歌网页搜索；其二，在所有调研国家中，谷歌图片搜索结果均呈现出范围狭窄、同质化严重的视觉表达体系特征。
这意味着网络认识论发生了从权威优先排序到相似性优先排序的转变，其驱动因素为计算性技术的更迭：从谷歌网页搜索依托的网页链接转向谷歌图片搜索依托的计算机视觉技术。本研究的发现与理论模型，为探究在图像信息日益饱和的数字社会中，计算机视觉技术对知识公共可及性的影响开辟了全新的研究方向。