Exploration of robust features of trust across multiple social networks

Zoheb H. Borbora, Muhammad Aurangzeb Ahmad, Karen Zita Haigh, Jaideep Srivastava, Zhen Wen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

In this paper, we investigate the problem of trust formation in virtual world interaction networks. The problem is formulated as one of link prediction, intranetwork and internetwork, in social networks. We use two datasets to study the problem - SOE's Everquest II MMO game dataset and IBM's SmallBlue sentiments dataset. We explore features based on the node's individual properties as well as based on the node's location within the network. In addition, we take into account the node's participation in other social networks within a specific prediction task. Different machine learning models built on the features are evaluated with the goal of finding a common set of features which are both robust and discriminating across the two datasets. Shortest Distance and Sum of Degree are found to be robust, discriminating features across the two datasets. Finally, based on experiment results and observations, we provide insights into the underlying online social processes. These insights can be extended to models for online social trust.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 5th IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW 2011
Pages27-32
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event2011 5th IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW 2011 - Ann Arbor, MI, United States
Duration: Oct 3 2011Oct 7 2011

Publication series

NameProceedings - 2011 5th IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW 2011

Other

Other2011 5th IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW 2011
CountryUnited States
CityAnn Arbor, MI
Period10/3/1110/7/11

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