PREDICTION OF USER'S TRUSTWORTHINESS INWEB-BASED SOCIAL NETWORKSVIA TEXT MINING

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MOHAMMADHASSANZADEH HOSSEIN; SHAHRIARI HAMID REZA

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Journal: THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY Year:2013 | Volume:5 | Issue:2 Page(s): 171-187

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Title

PREDICTION OF USER'S TRUSTWORTHINESS INWEB-BASED SOCIAL NETWORKSVIA TEXT MINING

Author(s)

MOHAMMADHASSANZADEH HOSSEIN | SHAHRIARI HAMID REZA | Issue Writer Certificate

Keywords

TRUSTQ2

REPUTATIONQ2

TEXT MININGQ2

USER SIMILARITYQ2

SOCIAL NETWORKSQ2

SIMILARITY MEASUREQ2

Abstract

In SOCIAL NETWORKS, users need a proper estimation of TRUST in others to beable to initialize reliable relationships. Some TRUST evaluation mechanisms havebeen o ered, which use direct ratings to calculate or propagate TRUST values. However, in some web-based SOCIAL NETWORKS where users only have binaryrelationships, there is no direct rating available. Therefore, a new method isrequired to infer TRUST values in these networks. To bridge this gap, this paperaims to propose a new method which takes advantage of USER SIMILARITY topredict TRUST values without any need for direct ratings. In this approach, which is based on socio-psychological studies, USER SIMILARITY is calculatedfrom the pro le information and the texts shared by the users via text-miningtechniques. Applying Ziegler ratios to our approach revealed that users aremore 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 TRUSTfrom language similarity. In addition, comparing the real assigned ratings, gathered directly from users, with the experimental results indicated that thepredicted TRUST values are su ciently acceptable (with a precision of 61%). Wehave also studied the bene ts of using context in inferring TRUST. In this regard, the analysis revealed that the precision of the predictions can be improved upto 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 mechanismto evaluate the correctness of TRUST values assigned by users, and increases therobustness of TRUST and REPUTATION mechanisms against possible security threats.

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APA: Copy

MOHAMMADHASSANZADEH, HOSSEIN, & SHAHRIARI, HAMID REZA. (2013). PREDICTION OF USER'S TRUSTWORTHINESS INWEB-BASED SOCIAL NETWORKSVIA TEXT MINING. THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY, 5(2 ), 171-187. SID. https://sid.ir/paper/241831/en

Vancouver: Copy

MOHAMMADHASSANZADEH HOSSEIN, SHAHRIARI HAMID REZA. PREDICTION OF USER'S TRUSTWORTHINESS INWEB-BASED SOCIAL NETWORKSVIA TEXT MINING. THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY[Internet]. 2013;5(2 ):171-187. Available from: https://sid.ir/paper/241831/en

IEEE: Copy

HOSSEIN MOHAMMADHASSANZADEH, and HAMID REZA SHAHRIARI, “PREDICTION OF USER'S TRUSTWORTHINESS INWEB-BASED SOCIAL NETWORKSVIA TEXT MINING,” THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY, vol. 5, no. 2 , pp. 171–187, 2013, [Online]. Available: https://sid.ir/paper/241831/en

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