Credibility Assessment of Public Pages over Facebook

With the growing use of online social media and presence of users on many such platforms, their interaction with social networks is huge. They are free to spread wrong information without any accuracy, integrity and authenticity checkpoints masquerading as legitimate content. All these wrong, unrelated, unwanted, manipulated information are distributed for some hidden reasons. Even, their distribution network is not limited to one social media platform. Sometimes, they use the social network as a market place either for advertising, promotion of particular website, product and an application. But these advertisements do not provide any incentive to Facebook as this content is just spam, irrelevant for Facebook. Dissemination of unwanted, unrelated information has become a huge problem not only on blog, discussion forum but also on online social network like Facebook. Due to lack of marking over content posted, this information become online and reader has no barometer to check either the credibility of commenter or poster or the credibility of facts. To address these issues, we have derived an equation to weigh the credibility of public pages and applied machine learning algorithms (MLA) over collected data to validate our prediction.

[1]  Amal Abdullah AlMansour,et al.  A MODEL FOR RECALIBRATING CREDIBILITY IN DIFFERENT CONTEXTS AND LANGUAGES - A TWITTER CASE STUDY , 2014 .

[2]  Miriam J. Metzger,et al.  Credibility for the 21st Century: Integrating Perspectives on Source, Message, and Media Credibility in the Contemporary Media Environment , 2003 .

[3]  Thomas M. Chen,et al.  Proliferation and Detection of Blog Spam , 2010, IEEE Security & Privacy.

[4]  Erdong Chen,et al.  Facebook immune system , 2011, SNS '11.

[5]  Thomas M. Chen,et al.  Malicious and Spam Posts in Online Social Networks , 2011, Computer.

[6]  Philip S. Yu,et al.  Diversionary Comments under Blog Posts , 2015, TWEB.

[7]  Sung-Un Yang,et al.  Measuring Social Media Credibility: A Study on a Measure of Blog Credibility , 2011 .

[8]  Rishabh Kaushal,et al.  Analysis of Text Mining Techniques over Public Pages of Facebook , 2016, 2016 IEEE 6th International Conference on Advanced Computing (IACC).

[9]  Nam P. Nguyen,et al.  Sources of misinformation in Online Social Networks: Who to suspect? , 2012, MILCOM 2012 - 2012 IEEE Military Communications Conference.

[10]  Gianluca Stringhini,et al.  Detecting spammers on social networks , 2010, ACSAC '10.

[11]  James M. Crafts.  Techniques for Memory Testing , 1979, Computer.

[12]  Shahrinaz Ismail,et al.  Authenticity Issues of Social Media: Credibility, Quality and Reality , 2013 .

[13]  Mohammad Ali Abbasi,et al.  Measuring User Credibility in Social Media , 2013, SBP.

[14]  Florian Michahelles,et al.  Monitoring Trends on Facebook , 2011, 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing.

[15]  Chen Lin,et al.  Identify content quality in online social networks , 2012, IET Commun..

[16]  Yin Pan,et al.  A social approach to security: Using social networks to help detect malicious web content , 2010, 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering.

[17]  Ponnurangam Kumaraguru,et al.  Towards automatic real time identification of malicious posts on Facebook , 2015, 2015 13th Annual Conference on Privacy, Security and Trust (PST).