@article{(Open Science Index):https://publications.waset.org/pdf/10000280,
	  title     = {Features for Measuring Credibility on Facebook Information},
	  author    = {Kanda Runapongsa Saikaew and  Chaluemwut Noyunsan},
	  country	= {},
	  institution	= {},
	  abstract     = {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.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {1},
	  year      = {2015},
	  pages     = {174 - 177},
	  ee        = {https://publications.waset.org/pdf/10000280},
	  url   	= {https://publications.waset.org/vol/97},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 97, 2015},
	}