Publications

@article{Mohammadhassanzadeh2013,

title = {Prediction of User’s Trustworthiness in Web-based Social Networks via Text Mining},

author = {Hossein Mohammadhassanzadeh and Hamid Reza Shahriari},

url = {http://isecure-journal.com/index.php/isecure/article/view/12-163/73},

year  = {2013},

date = {2013-07-01},

journal = {ISecure Journal},

volume = {5},

number = {2},

pages = {171-187},

abstract = {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 increases the robustness of trust and reputation mechanisms against possible security threats.},

keywords = {Data Mining, Social Network Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{Stewart:JMedInternetRes:2012,

title = {Applying Social Network Analysis to Understand the Knowledge Sharing Behaviour of Practitioners in a Clinical Online Discussion Forum.},

author = {Samuel Alan Stewart and Syed Sibte Raza Abidi},

issn = {1438-8871},

year  = {2012},

date = {2012-01-01},

journal = {Journal of Medical Internet Research},

volume = {14},

number = {6},

pages = {e170},

address = {Canada},

abstract = {BACKGROUND: Knowledge Translation (KT) plays a vital role in the modern health care community, facilitating the incorporation of new evidence into practice. Web 2.0 tools provide a useful mechanism for establishing an online KT environment in which health practitioners share their practice-related knowledge and experiences with an online community of practice. We have implemented a Web 2.0 based KT environment--an online discussion forum--for pediatric pain practitioners across seven different hospitals in Thailand. The online discussion forum enabled the pediatric pain practitioners to share and translate their experiential knowledge to help improve the management of pediatric pain in hospitals. OBJECTIVE: The goal of this research is to investigate the knowledge sharing dynamics of a community of practice through an online discussion forum. We evaluated the communication patterns of the community members using statistical and social network analysis methods in order to better understand how the online community engages to share experiential knowledge. METHODS: Statistical analyses and visualizations provide a broad overview of the communication patterns within the discussion forum. Social network analysis provides the tools to delve deeper into the social network, identifying the most active members of the community, reporting the overall health of the social network, isolating the potential core members of the social network, and exploring the inter-group relationships that exist across institutions and professions. RESULTS: The statistical analyses revealed a network dominated by a single institution and a single profession, and found a varied relationship between reading and posting content to the discussion forum. The social network analysis discovered a healthy network with strong communication patterns, while identifying which users are at the center of the community in terms of facilitating communication. The group-level analysis suggests that there is strong interprofessional and interregional communication, but a dearth of non-nurse participants has been identified as a shortcoming. CONCLUSIONS: The results of the analysis suggest that the discussion forum is active and healthy, and that, though few, the interprofessional and interinstitutional ties are strong},

keywords = {Experiential Knowledge, Knowledge Sharing, Knowledge Translation, Physician-Patient Relations, Social Network Analysis},

pubstate = {published},

tppubtype = {article}

}