A Deeper Investigation of the Importance of Wikipedia Links to Search Engine Results

Vincent, Nicholas; Hecht, Brent

doi:10.1145/3449078

Cited by 20 publications

(9 citation statements)

References 16 publications

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“…A feminist or anti-racist Google Search would likely have difficulties stabilizing as its political position would be challenged. So instead, Google Search tries to keep up the appearance of being a neutral platform (Gillespie 2010) and relying on other platforms that are considered uncontroversial by many such as Wikipedia (Vincent and Hecht 2021), or platforms that derive authority through voting and numbers such as Yelp or IMDB. This often means in practice that many results adhere to a hegemonic white individualist male gaze (Noble 2018;Bender et al 2021).…”

Section: Claiming Constructed Results Encompass Mulitplicit Contingen...mentioning

confidence: 99%

Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future

Grill¹

2022

Preprint

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The use of opaque machine learning algorithms is often justified by their accuracy. For example, IBM has advertised its algorithms as being able to predict when workers will quit with 95% accuracy, an EU research project on lie detection in border control has reported 75% accuracy, and researchers have claimed to be able to deduce sexual orientation with 91% accuracy from face images. Such performance numbers are, on the one hand, used to make sense of the functioning of opaque algorithms and promise to quantify the quality of algorithmic predictions. On the other hand, they are also performative, rhetorical, and meant to convince others of the ability of algorithms to know the world and its future objectively, making calculated, partial visions appear certain. This duality marks a conflict of interest when the actors who conduct an evaluation also profit from positive outcomes. Building on work in the sociology of testing and agnotology, I discuss seven ways how the construction of high accuracy claims also involves the production of ignorance. I argue that this ignorance should be understood as productive and strategic as it is imbued with epistemological authority by making uncertain matters seem certain in ways that benefit some groups over others. Several examples illustrate how tech companies increasingly strategically produce ignorance reminiscent of tactics used by controversial companies with a high concentration of market power such as big oil or tobacco. My analysis deconstructs claims of certainty by highlighting the politics and contingencies of testing used to justify the adoption of algorithms. I further argue that current evaluation practices in ML are prone to producing problematic forms of ignorance, like misinformation, and reinforcing structural inequalities due to how human judgment and power structures are invisibilized, narrow, oversimplified metrics overused, and pernicious incentive structures encourage overstatements enabled by flexibility in testing. I provide recommendations on how to deal with and rethink incentive structures, testing practices, and the communication and study of accuracy with the goal of opening possibilities, making contingencies more visible, and enabling the imagination of different futures.

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Section: Claiming Constructed Results Encompass Mulitplicit Contingen...mentioning

confidence: 99%

Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future

Grill¹

2022

Preprint

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“…They observe that Wikipedia links appear in over 80% of SERPs for some query types which leads them to the conclusion that Wikipedia's user generated content is invaluable to Google and that no other website in the world is as dependent on Wikipedia as Google is (Vincent et al 2019). Most recently, Vincent and Hecht go yet one step further and include mobile devices and the two additional search engines, Bing and DuckDuckGo, in a deeper investigation of the importance of Wikipedia's content for SERPs (Vincent and Hecht 2021). Via a search algorithm audit study, they again prove that Wikipedia content has much influence outside of Wikipedia and that all search engines -not only Google -rely to a large degree on the encyclopedia's content, highlighting again how important user generated content by volunteers can be.…”

Section: Wikipedia Text Reusementioning

confidence: 99%

“…First and foremost, however, Wikipedia is significantly shaping our digital experience. Most state-of-the-art machine learning algorithms are trained on Wikipedia content (Devlin et al 2019;Cambazoglu et al 2021), millions of searchers are dependent on Google surfacing Wikipedia links to satisfy their information needs (McMahon, Johnson, and Hecht 2017;Vincent et al 2019;Vincent and Hecht 2021), tech giants are using human workforce and Wikipedia for fact checking on their platforms (Flynn 2017; Perez 2020), Wikipedia links are among the most prominent URLs on community question answering sites like Reddit or Stack Overflow (Gómez, Cleary, and Singer 2013;Vincent, Johnson, and Hecht 2018), voice-operated virtual assistants like Siri and Alexa rely heavily on Wikipedia (Vincent and Hecht 2021) and for many users Wikipedia is a stepping stone or gateway to the larger web itself (Piccardi et al 2021). While Wikipedia is providing a lot of value to other platforms, less value is flowing back to Wikipedia in return.…”

Section: Introductionmentioning

confidence: 99%

TWikiL - Twitter Wikipedia Link Dataset

Meier

2022

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Recent research has shown how strongly Wikipedia and other web services or platforms are connected. For example, search engines rely heavily on surfacing Wikipedia links to satisfy their users' information needs and volunteer-created Wikipedia content frequently gets re-used on other social media platforms like Reddit. However, publicly accessible datasets that enable researchers to study the interrelationship between Wikipedia and other platforms are sparse. In addition to that, most studies only focus on certain points in time and don't consider the historical perspective. To begin solving these problems we developed TWikiL, the Twitter Wikipedia Link Dataset, which contains all Wikipedia links posted on Twitter in the period 2006 to January 2021. We extract Wikipedia links from Tweets and enrich the referenced articles with their respective Wikidata identifiers and Wikipedia topic categories, which will make this dataset immediately useful for a large range of scholarly use cases. In this paper, we describe the data collection process, perform an initial exploratory analysis and present a comprehensive overview of how this dataset can be useful for the research community.

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“…Wikipedia is one of the first results for most of the queries on U.S.-based search engines such as Google or Bing (Vincent & Hecht, 2021). It provides vast access to information, both as texts and images.…”

Section: Wikimedia Projects As Test Fieldsmentioning

confidence: 99%

An Open Toolkit for Small-Scale Data Visualizations

Pronzati

Mauri

2022

Diid

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Wikipedia represents an open and collaborative encyclopedic environment, and is one of the main sources of information today. The information is encoded, among other formats, in graphics and data visualizations. The source of such pieces of information is various — some are taken from governmental sources, or reports published under Creative Commons licenses, others are created and uploaded by the platform users. Given the current UI of the Wikipedia platform, however, most of these contents are relegated to small thumbnails, making them difficult to read. In the research presented here we propose an open toolkit for the design of data visualizations in small spaces — such as thumbnails — conceived for the project’s contributors. By engaging with the design of visualizations for the platform and contributing to the project, it was possible to identify good practices that were then synthesized in a toolkit openly accessible to all contributors.

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A Deeper Investigation of the Importance of Wikipedia Links to Search Engine Results

Cited by 20 publications

References 16 publications

Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future

Constructing Certainty in Machine Learning: On the performativity of testing and its hold on the future

TWikiL - Twitter Wikipedia Link Dataset

An Open Toolkit for Small-Scale Data Visualizations

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