Features for Measuring Credibility on Facebook Information

Authors: Kanda Runapongsa Saikaew, Chaluemwut Noyunsan

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.

Keywords: Facebook, social media, credibility measurement.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338034

References:

[1] Statista (2014). Global social networks ranked by number of users 2014(Online). Available: http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/.
[2] M. R. Morris, S. Counts, A. Roseway, A. Hoff , J. Schwarz, “Tweeting is believing?: understanding microblog credibility perceptions”, Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, February 11-15, 2012, Seattle, Washington, USA
[3] C. Castillo, M. Mendoza, B. Poblete, “Information credibility on twitter”,In Proc. of the international conference on World wide web, pp. 675-684, ACM, 2011.
[4] A. Gupta, P. Kumaraguru, “Credibility ranking of tweets during high impact events”, In Proc. 1st Workshop on Privacy and Security in Online Social Media, PSOSM '12,pages 2:2-2:8. ACM, 2012.
[5] A. Gupta, H. Lamba, P. Kumaraguru, A. Joshi, “Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy”, In Proc. WWW companion, pages 729-736. International World Wide Web Conferences Steering Committee, 2013.
[6] R. M. B. Al-Eidan, H. S. Al-Khalifa, A. S. Al-Salman, “Measuring the credibility of Arabic text content in Twitter”, ICDIM 2010: 285-291
[7] Yukino Ikegami, Kenta Kawai, Yoshimi Namihira and SetsuoTsuruta, "Topic and Opinion Classification based Information Credibility Analysis on Twitter", 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4676-4681, 2013.
[8] A. Gupta, P. Kumaraguru, C. Castillo, P. Meier, “TweetCred: A Real-time Web-based System for Assessing Credibility of Content on Twitter”, arxiv, 2014.
[9] C.-C. Chang, C.-J. Lin, “LIBSVM : a library for support vector machines”, ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.