TY - JFULL AU - Kanda Runapongsa Saikaew and Chaluemwut Noyunsan PY - 2015/2/ TI - Features for Measuring Credibility on Facebook Information T2 - International Journal of Computer and Information Engineering SP - 173 EP - 177 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10000280 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 97, 2015 N2 - Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system. ER -