TY - JOUR ID - 39144 TI - Prediction of user's trustworthiness in web-based social networks via text mining JO - The ISC International Journal of Information Security JA - ISECURE LA - en SN - 2008-2045 AU - Mohammadhassanzadeh, H. AU - Shahriari, H. R. AD - Y1 - 2013 PY - 2013 VL - 5 IS - 2 SP - 171 EP - 187 KW - Trust KW - Reputation KW - Text Mining KW - User Similarity KW - Social Networks KW - similarity measure DO - 10.22042/isecure.2014.5.2.5 N2 - In Social networks, users need a proper estimation of trust in others to be able to initialize reliable relationships. Some trust evaluation mechanisms have been offered, which use direct ratings to calculate or propagate trust values. However, in some web-based social networks where users only have binary relationships, there is no direct rating available. Therefore, a new method is required to infer trust values in these networks. To bridge this gap, this paper aims to propose a new method which takes advantage of user similarity to predict trust values without any need for direct ratings. In this approach, which is based on socio-psychological studies, user similarity is calculated from the profile information and the texts shared by the users via text-mining techniques. Applying Ziegler ratios to our approach revealed that users are more than 50% more similar to their trusted agents than to arbitrary peers, which proves the validity of the original idea of the study about inferring trust from language similarity. In addition, comparing the real assigned ratings, gathered directly from users, with the experimental results indicated that the predicted trust values are sufficiently acceptable (with a precision of 61%). We have also studied the benefits of using context in inferring trust. In this regard, the analysis revealed that the precision of the predictions can be improved up to 72%. Besides the application of this approach in web-based social networks, the proposed technique can also be of much help in any direct rating mechanism to evaluate the correctness of trust values assigned by users, and increase the robustness of trust and reputation mechanisms against possible security threats. UR - https://www.isecure-journal.com/article_39144.html L1 - https://www.isecure-journal.com/article_39144_07ace155037ae5cd378df8eb32cb7239.pdf ER -