谷歌、数据空洞和排斥政治的动态

IF 6.5 1区社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY

Big Data & Society Pub Date : 2023-01-01 DOI:10.1177/20539517221149099

Ov Cristian Norocel, D. Lewandowski

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引用次数: 3

摘要

这项研究采用了一种关键的大数据分析方法，以衡量谷歌搜索中数据空白的初步轮廓，这些数据空白反映了2015年欧洲人道主义危机后极右翼的排斥动态。这项研究增加了数据空白分析的复杂性，通过将重点放在德国和瑞典，将调查框架扩展到了美国之外。在之前针对排斥政治的大数据分析的基础上，该研究提出了一系列关于德国和瑞典移民问题的问题，从主流词汇到极右翼词汇。此查询目录使特定的本地化查询能够识别数据空白。结果表明，搜索引擎对来源受欢迎程度的依赖可能导致极右翼来源出现在最高位置。此外，使用用户生成内容的平台为本地化查询提供了一种获得最高职位的方式。

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Google, data voids, and the dynamics of the politics of exclusion

This study deploys a critical approach to big data analytics to gauge the tentative contours of data voids in Google searches that reflect extreme-right dynamics of exclusion in the aftermath of the 2015 humanitarian crisis in Europe. The study adds complexity to the analysis of data voids, expanding the framework of investigation outside the USA context by concentrating on Germany and Sweden. Building on previous big data analytics addressing the politics of exclusion, the study proposes a catalogue of queries concerning the issue of migration in both Germany and Sweden on a continuum from mainstream to extreme-right vocabularies. This catalogue of queries enables specific and localized queries to identify data voids. The results show that a search engine's reliance on source popularity may lead to extreme-right sources appearing in top positions. Furthermore, using platforms for user-generated content provides a way for localized queries to gain top positions.

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来源期刊

Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

10.90

自引率

10.60%

发文量

审稿时长

11 weeks

期刊介绍： Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.