Automated decision support for human tasks in a collaborative system

Gelley, Bluma; Suel, Torsten

doi:10.1145/2491055.2491084

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…The user base is dominated by visitors from India and the United States, who together account for more than 60% of the total traffic. 16 In a related scenario of analyzing online content, [52] finds that author features have a strong discriminative power. 17 We observed several answers that were blurred out with images or only partially collected by the crawl, which we omitted.…”

Section: Discussion a Limitations Of The Study Due To Dataset Cmentioning

confidence: 99%

Cloak and Swagger: Understanding Data Sensitivity through the Lens of User Anonymity

Peddinti

Korolova

Bursztein

et al. 2014

2014 IEEE Symposium on Security and Privacy

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Abstract-Most of what we understand about data sensitivity is through user self-report (e.g., surveys); this paper is the first to use behavioral data to determine content sensitivity, via the clues that users give as to what information they consider private or sensitive through their use of privacy enhancing product features. We perform a large-scale analysis of user anonymity choices during their activity on Quora, a popular question-and-answer site. We identify categories of questions for which users are more likely to exercise anonymity and explore several machine learning approaches towards predicting whether a particular answer will be written anonymously. Our findings validate the viability of the proposed approach towards an automatic assessment of data sensitivity, show that data sensitivity is a nuanced measure that should be viewed on a continuum rather than as a binary concept, and advance the idea that machine learning over behavioral data can be effectively used in order to develop product features that can help keep users safe.

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Section: Discussion a Limitations Of The Study Due To Dataset Cmentioning

confidence: 99%

Cloak and Swagger: Understanding Data Sensitivity through the Lens of User Anonymity

Peddinti

Korolova

Bursztein

et al. 2014

2014 IEEE Symposium on Security and Privacy

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“…(2012) observed that editing conflicts are typically ended by “sheer exhaustion, the evident numerical dominance of one group, or admin intervention.” To dominate a controversial article in the face of resistance, a number of editors sufficient to dominate the discussion must gain support from each other, and/or from admin(s) who can overrule resisting editors (Martin, 2018). WP editors maintaining bias monitor articles of interest and act quickly (within hours) to “protect” these articles, often by reverts (Gelley and Suel, 2013; Sundin, 2011). These editors tend to rely heavily on citing WP rules and banning resisting editors through admins to avoid consensus building processes (Koppelman, 2017; Martin, 2018).…”

Section: Problem Statementmentioning

confidence: 99%

The colonization of Wikipedia: evidence from characteristic editing behaviors of warring camps

Morris-O'Connor

Strotmann²,

Zhao³

2022

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PurposeTo add new empirical knowledge to debates about social practices of peer production communities, and to conversations about bias and its implications for democracy. To help identify Wikipedia (WP) articles that are affected by systematic bias and hopefully help alleviate the impact of such bias on the general public, thus helping enhance both traditional (e.g. libraries) and online information services (e.g. Google) in ways that contribute to democracy. This paper aims to discuss the aforementioned objectives.Design/methodology/approachQuantitatively, the authors identify edit-warring camps across many conflict zones of the English language WP, and profile and compare success rates and typologies of camp edits in the corresponding topic areas. Qualitatively, the authors analyze the edit war between two senior WP editors that resulted in imbalanced and biased articles throughout a topic area for such editorial characteristics through a close critical reading.FindingsThrough a large-scale quantitative study, the authors find that winner-take-all camps exhibit biasing editing behaviors to a much larger extent than the camps they successfully edit-war against, confirming findings of prior small-scale qualitative studies. The authors also confirm the employment of these behaviors and identify other behaviors in the successful silencing of traditional medicinal knowledge on WP by a scientism-biased senior WP editor through close reading.Social implicationsWP sadly does, as previously claimed, appear to be a platform that represents the biased viewpoints of its most stridently opinionated Western white male editors, and routinely misrepresents scholarly work and scientific consensus, the authors find. WP is therefore in dire need of scholarly oversight and decolonization.Originality/valueThe authors independently verify findings from prior personal accounts of highly power-imbalanced fights of scholars against senior editors on WP through a third-party close reading of a much more power balanced edit war between senior WP editors. The authors confirm that these findings generalize well to edit wars across WP, through a large scale quantitative analysis of unbalanced edit wars across a wide range of zones of contention on WP.

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“…To dominate a controversial article in the face of resistance, a number of editors sufficient to dominate the discussion must gain support from each other, and/or from admin(s) who can overrule resisting editors (Martin, 2018). WP editors maintaining bias monitor articles of their interests and act quickly (within hours) to "protect" these articles, often by reverts (Gelley & Suel, 2013;Sundin, 2011). These editors tend to rely heavily on citing WP rules and banning resisting editors through admins to avoid consensus building processes (Koppelman, 2017;Martin, 2018).…”

Section: Editing Strategies To Introduce and Maintain Bias On Wikipediamentioning

confidence: 99%

Editorial Behaviors for Biasing Wikipedia Articles

Morris-O'Connor

Strotmann²,

Zhao

2022

Proceedings of the Association for Information Science and Tech

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As the vital source of information on the Web that it has long become, Wikipedia (WP) has developed detailed policies and guidelines for contributing to it as well as for resolving conflicts with a goal to ensure that all important viewpoints are represented fairly and supported by trustworthy published resources. However, WP has long been criticized for systemic biases in its content, which are often implemented and maintained through the use of these same WP policies and guidelines. The present study explores editorial behaviors of a selected editor who succeeded in biasing a set of WP articles. By selecting an editor to examine through a big‐data approach, this study can serve as an objective crosscheck for previously reported tactics for introducing and maintaining bias observed from personal experiences of disputes on WP, and could provide evidence to generate empirically grounded hypotheses concerning editorial practices indicative of bias. It was found that editorial behavior around deletion and erasure is an important strategy for implementing and maintaining bias on WP, and WP policies and guidelines are used to support and validate the silencing of other voices.

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Automated decision support for human tasks in a collaborative system

Cited by 6 publications

References 22 publications

Cloak and Swagger: Understanding Data Sensitivity through the Lens of User Anonymity

Cloak and Swagger: Understanding Data Sensitivity through the Lens of User Anonymity

The colonization of Wikipedia: evidence from characteristic editing behaviors of warring camps

Editorial Behaviors for Biasing Wikipedia Articles

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