Extracting Trust from Domain Analysis: A Case Study on the Wikipedia Project

Dondio, Pierpaolo; Barrett, Stephen; Weber, Stefan; Seigneur, Jean Marc

doi:10.1007/11839569_35

Cited by 51 publications

(51 citation statements)

References 8 publications

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“…The proliferation of collaborative environments, for example the ones based on Wiki 4 , represent good examples in which Computational Trust paradigms are applied in order to evaluate the trustworthiness of virtual identities. Dondio et al [7] performed a set of experiments by mapping onto a model of Wikipedia expertise derived from studies in collaborative editing and content quality. Longo et al [17] conceived a set of trust evidences based on time and applied to encyclopaedia, demonstrating how plausible trust decisions can be reached using exclusively temporal factors.…”

Section: Computational Trustmentioning

confidence: 99%

Enhancing Social Search: A Computational Collective Intelligence Model of Behavioural Traits, Trust and Time

Longo

Dondio

Barrett

2010

Lecture Notes in Computer Science

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Abstract. The Web has been growing in size and with the proliferation of large-scale collaborative computing environments, Social Search has become increasingly important. This recent field focuses on assigning relevance to Web-pages by considering the reader's perspective rather than Web-masters' point of view. Current searching technologies of this form tend to rely on explicit human recommendations. In part because it is hard to obtain user feedback, these methods are hard to scale. The challenge is in producing implicit rankings, by reasoning over users' Websearch activity, without recourse to explicit human intervention. This paper focuses on a novel Social Search model based on Information Foraging Theory, Effort and Computational Trust, showing a different way to implicitly judge Web-entities. The formalism has been divided in two sub-models. The first considers the effort expended by users, in viewing Web-sites, to assess their relevance to a given searching problem. The second enhances the first sub-model by considering only the most trustworthy users' opinions, identified by Computational Trust techniques. 100 university students explicitly evaluated the usefulness of 12 thematic Web-sites and their browsing activity were gathered. Correlation indexes suggests the existence of a considerable relationship between explicit feedback and implicit computed judgements that were further compared to the ones, provided by the Google search engine. The proposed nature-inspired approach suggests that, by considering the same searching query, Social Search to be more effective than the Google Page-Rank Algorithm. The consideration of the only identified trustworthy students' implicit feedback provides a way to increase the accuracy of the effortbased approach. This evidence supports the presentation of a schema for a Social Search engine that generates implicit rankings by considering the collective intelligence emerged from users on the Web.

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Section: Computational Trustmentioning

confidence: 99%

Enhancing Social Search: A Computational Collective Intelligence Model of Behavioural Traits, Trust and Time

Longo

Dondio

Barrett

2010

Lecture Notes in Computer Science

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“…Related Works: The work most similar to Stvilia's is that of Dondio et al [28]. Dondio begins by formally modeling the Wikipedia infrastructure and identifying ten "propositions about trustworthiness of articles" which are essentially IQ metrics.…”

Section: Content-exclusive Featuresmentioning

confidence: 99%

Trust in collaborative web applications

West

Chang

Venkatasubramanian

et al. 2012

Future Generation Computer Systems

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Collaborative functionality is increasingly prevalent in web applications. Such functionality permits individuals to add -and sometimes modify -web content, often with minimal barriers to entry. Ideally, large bodies of knowledge can be amassed and shared in this manner. However, such software also provide a medium for nefarious persons to operate. By determining the extent to which participating content/agents can be trusted, one can identify useful contributions. In this work, we define the notion of trust for Collaborative Web Applications and survey the state-of-the-art for calculating, interpreting, and presenting trust values. Though techniques can be applied broadly, Wikipedia's archetypal nature makes it a focal point for discussion. KeywordsCollaborative web applications, trust, reputation, Wikipedia AbstractCollaborative functionality is increasingly prevalent in web applications. Such functionality permits individuals to add -and sometimes modify -web content, often with minimal barriers to entry. Ideally, large bodies of knowledge can be amassed and shared in this manner. However, such software also provide a medium for nefarious persons to operate. By determining the extent to which participating content/agents can be trusted, one can identify useful contributions. In this work, we define the notion of trust for Collaborative Web Applications and survey the state-of-the-art for calculating, interpreting, and presenting trust values. Though techniques can be applied broadly, Wikipedia's archetypal nature makes it a focal point for discussion.

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“…This approach was the one followed for calculating the reputation of content and users in wikis recently: Dondio et al suggested an algorithm to automatically calculate the overall trustworthiness of a wiki article based on statistical data [5]. Adler and Alfaro [6] present a system for Wikipedia that computes an editor's reputation by observing whether subsequent users preserve the changes that were made.…”

Section: Centralized Communication Protocols That Allow Users Tomentioning

confidence: 99%

“…applications, e.g., electronic marketplaces like eBay 5 can be used to help wiki users to build up a reputation about their expertise, that they can use as authors and raters of articles. Having a user's reputation publicly available helps others to assess the trustworthiness of this user.…”

mentioning

confidence: 99%

Privacy-Respecting Reputation for Wiki Users

2011

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Wikis are popular tools for creation and sharing of content. Integrated reputation systems allow to assess expertise and reliability of authors and thus to support trust in the wiki content. Yet, results from our empirical study indicate that the disclosure of user reputation evokes privacy issues. As a solution for this conflict between the need to evaluate trustworthiness of users and protecting their privacy, we present a privacy-respecting reputation system for wikis that we realized as OpenSource-Extension for the wiki software MediaWiki.

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Extracting Trust from Domain Analysis: A Case Study on the Wikipedia Project

Cited by 51 publications

References 8 publications

Enhancing Social Search: A Computational Collective Intelligence Model of Behavioural Traits, Trust and Time

Enhancing Social Search: A Computational Collective Intelligence Model of Behavioural Traits, Trust and Time

Trust in collaborative web applications

Privacy-Respecting Reputation for Wiki Users

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