Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Measuring article quality in wikipedia: models and evaluation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Size matters: word count as a measure of quality on wikipedia
Proceedings of the 17th international conference on World Wide Web
Computing trust from revision history
Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services
Computing information retrieval performance measures efficiently in the presence of tied scores
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Assigning trust to Wikipedia content
WikiSym '08 Proceedings of the 4th International Symposium on Wikis
Comparative trust management with applications: Bayesian approaches emphasis
Future Generation Computer Systems
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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.