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Quantifying the trustworthiness of social media content
Abstract The growing popularity of social media in The growing popularity of social media in recent years has resulted in the creation of an enormous amount of user-generated content. A significant portion of this information is useful and has proven to be a great source of knowledge. However, since much of this information has been contributed by strangers with little or no apparent reputation to speak of, there is no easy way to detect whether the content is trustworthy. Search engines are the gateways to knowledge but search relevance cannot guarantee that the content in the search results is trustworthy. A casual observer might not be able to differentiate between trustworthy and untrustworthy content. This work is focused on the problem of quantifying the value of such shared content with respect to its trustworthiness. In particular, the focus is on shared health content as the negative impact of acting on untrustworthy content is high in this domain. Health content from two social media applications, Wikipedia and Daily Strength, is used for this study. Sociological notions of trust are used to motivate the search for a solution. A two-step unsupervised, feature-driven approach is proposed for this purpose: a feature identification step in which relevant information categories are specified and suitable features are identified, and a quantification step for which various unsupervised scoring models are proposed. Results indicate that this approach is effective and can be adapted to disparate social media applications with ease.arate social media applications with ease.
Abstractsub The growing popularity of social media in The growing popularity of social media in recent years has resulted in the creation of an enormous amount of user-generated content. A significant portion of this information is useful and has proven to be a great source of knowledge. However, since much of this information has been contributed by strangers with little or no apparent reputation to speak of, there is no easy way to detect whether the content is trustworthy. Search engines are the gateways to knowledge but search relevance cannot guarantee that the content in the search results is trustworthy. A casual observer might not be able to differentiate between trustworthy and untrustworthy content. This work is focused on the problem of quantifying the value of such shared content with respect to its trustworthiness. In particular, the focus is on shared health content as the negative impact of acting on untrustworthy content is high in this domain. Health content from two social media applications, Wikipedia and Daily Strength, is used for this study. Sociological notions of trust are used to motivate the search for a solution. A two-step unsupervised, feature-driven approach is proposed for this purpose: a feature identification step in which relevant information categories are specified and suitable features are identified, and a quantification step for which various unsupervised scoring models are proposed. Results indicate that this approach is effective and can be adapted to disparate social media applications with ease.arate social media applications with ease.
Bibtextype article  +
Doi 10.1007/s10619-010-7077-0  +
Has author Moturu S.T. + , Hongyan Liu +
Has extra keyword Content + , Feature identification + , Negative impacts + , Scoring models + , Search results + , Social media + , Trust evaluation + , Trustworthiness + , User generated content + , Wikipedia + , Search engine +
Has keyword Content + , Quality + , Social media + , Trust evaluation + , Trustworthiness +
Issn 9268782  +
Issue 3  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 239–260  +
Published in Distributed and Parallel Databases +
Title Quantifying the trustworthiness of social media content +
Type journal article  +
Volume 29  +
Year 2011 +
Creation dateThis property is a special property in this wiki. 8 November 2014 05:03:33  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 8 November 2014 05:03:33  +
DateThis property is a special property in this wiki. 2011  +
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