| 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 the trustworthy and the untrustworthy content. This dissertation 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 other social media applications with ease. The same approach is also applied successfully to the related problem of quantifying the usefulness or utility of content shared in response to a specific request. Trust and utility scores generated from these models can serve as essential supplements to search relevance scores. Together, these would provide social media consumers with a much improved experience by assisting them in finding better information faster and with confidence, and also benefit the social media community and promote its development in the long run. |
