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Quantifying Utility and Trustworthiness for Advice Shared on Online Social Media

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3 Author(s)
Sai T. Moturu ; Comput. Sci. & Eng., Arizona State Univ., Tempe, AZ, USA ; Jian Yang ; Huan Liu

The growing popularity of social media in recent years has resulted in the creation of an enormous amount of user-developed content. While information is readily available, there is no easy way to find the most useful content or to detect whether it is trustworthy. A casual observer might not be able to differentiate between the useful and the useless or the trustworthy and the untrustworthy. In this work, we wish to study the problem of quantifying the value of such user-shared content. In particular, we are focussed on health content as the negative impacts are higher for this domain. We use advice shared on a health social network, Daily Strength, for this study. We describe and define the notions of trustworthiness and utility for social media content. We identify the necessity and challenges for their assessment, and propose a framework that helps address these challenges by identifying relevant features and providing empirical means to meet the requirements for such an evaluation. We select relevant variables and perform numerous experiments to evaluate our models. The results demonstrate promising performance that could possibly be replicated with other social media applications.

Published in:

Computational Science and Engineering, 2009. CSE '09. International Conference on  (Volume:4 )

Date of Conference:

29-31 Aug. 2009