We identify robust features of trust in social networks; these are features which are discriminating yet uncorrelated and can potentially be used to predict trust formation between agents in other social networks. The features we investigate are based on an agent’s individual properties as well as those based on the agent’s location within the network. In addition, we analyze features which take into account the agent’s participation in other social interactions within the same network. Three datasets were used in our study—Sony Online Entertainment’s EverQuest II game dataset, a large email network with sentiments and the publicly available Epinions dataset. The first dataset captures activities from a complex persistent game environment characterized by several types of in-game social interactions, whereas the second dataset has anonymized information about people’s email and instant messaging communication. We formulate the problem as one of the link predictions, intranetwork and internetwork, in social networks. We first build machine learning models and then perform an ablation study to identify robust features of trust. Results indicate that shared skills and interests between two agents, their level of activity and level of expertise are the top three predictors of trust in a social network. Furthermore, if only network topology information were available, then an agent’s propensity to connect or communicate, the cosine similarity between two agents and shortest distance between them are found to be the top three predictors of trust. In our study, we have identified the generic characteristics of the networks used as well as the features investigated so that they can be used as guidelines for studying the problem of predicting trust formation in other social networks.
@article{borbora2013robust,title={Robust features of trust in social networks},author={Borbora, Zoheb Hassan and Ahmad, Muhammad Aurangzeb and Oh, Jehwan and Haigh, Karen Zita and Srivastava, Jaideep and Wen, Zhen},journal={Social Network Analysis and Mining},volume={3},pages={981--999},year={2013},publisher={Springer},}
Journal
Trust, distrust and lack of confidence of users in online social media-sharing communities
With the proliferation of online communities, the deployment of knowledge, skills, experiences and user generated content are generally facilitated among participant users. In online social media-sharing communities, the success of social interactions for content sharing and dissemination among completely unknown users depends on ’trust’. Therefore, providing a satisfactory trust model to evaluate the quality of content and to recommend personalized trustworthy content providers is vital for a successful online social media-sharing community. Current research on trust prediction strongly relies on a web of trust, which is directly collected from users. However, the web of trust is not always available in online communities and, even when it is available, it is often too sparse to accurately predict the trust value between two unacquainted people. Moreover, most of the extant trust research studies have not paid attention to the importance of distrust, even though distrust is a distinct concept from trust with different impacts on behavior. In this paper, we adopt the concepts of ’trust’, ’distrust’, and ’lack of confidence’ in social relationships and propose a novel unifying framework to predict trust and distrust as well as to distinguish the confidently-made decisions (trust or distrust) from lack of confidence without a web of trust. This approach uses interaction histories among users including rating data that is available and much denser than explicit trust/distrust statements (i.e. a web of trust).
@article{kim2013trust,title={Trust, distrust and lack of confidence of users in online social media-sharing communities},author={Kim, Young Ae and Ahmad, Muhammad A},journal={Knowledge-Based Systems},volume={37},pages={438--450},year={2013},publisher={Elsevier},}
Trust is a ubiquitous phenomenon in human societies. Computational trust refers to the mediation of trust via a computational infrastructure. It has been studied in a variety of contexts e.g., peer-to-peer systems, multi-agent systems, recommendation systems etc. While this is an active area of research, the types of questions that have been explored in this eld has been limited mainly because of limitations in the types of datasets which are available to researchers. In this thesis questions related to trust in complex social environments represented by Massively Multiplayer Online Games (MMOGs) are explored. The main emphasis is that trust is a multi-level phenomenon both in terms of how it operates at multiple levels of network granularities and how trust relates to other social phenomenon like homophily, expertise, mentoring, clandestine behaviors etc. Social contexts and social environments aect not just the qualitative aspects of trust but this phenomenon is also manifested with respect to the network and structural signatures of trust network Additionally trust is also explored in the context of predictive tasks: Previously described prediction tasks like link prediction are studied in the context of trust within the context of the link prediction family of problems: Link formation, link breakage, change in links etc. Additionally we dene and explore new trust-related prediction problems i.e., trust propensity prediction, trust prediction across networks which can be generalized to the inter-network link prediction problem and success prediction based on using network measures of a person’s social capital as a proxy.
@book{ahmad2012computational,title={Computational trust in multiplayer online games},author={Ahmad, Muhammad Aurangzeb},year={2012},publisher={University of Minnesota},url={https://ieeexplore.ieee.org/abstract/document/6406262},}
2011
Social Computing
Trust me, i’m an expert: Trust, homophily and expertise in mmos
Muhammad Aurangzeb
Ahmad, Iftekhar
Ahmed, Jaideep
Srivastava, and
1 more author
In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, 2011
Trust is a ubiquitous phenomenon in social networks and people trust one another for a variety of reasons. In this paper we study the problem of trust in massively multiplayer online games (MMOs) with respect to homophily and expertise. We prose a topology of homophily in MMOs based on the literature on homophily and domain knowledge of MMOs. Our results show that while there is some mapping between homophily in MMOs and the theories of homophily in the offline world, the mapping is not complete. Only ascribed homophily and value homophily is observed in the trust network, while other types of homophilies are conspicuously absent. We observed that the trust network exhibits many properties which are not observed in most other social networks. Based on our observations we propose a generative model for trust networks in MMOs.
@inproceedings{ahmad2011trust,title={Trust me, i'm an expert: Trust, homophily and expertise in mmos},author={Ahmad, Muhammad Aurangzeb and Ahmed, Iftekhar and Srivastava, Jaideep and Poole, Marshall Scott},booktitle={2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing},pages={882--887},year={2011},organization={IEEE},}
IEEE SASOW
Exploration of robust features of trust across multiple social networks
Zoheb H
Borbora, Muhammad Aurangzeb
Ahmad, Karen Zita
Haigh, and
2 more authors
In 2011 Fifth IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops, 2011
In this paper, we investigate the problem of trust formation in virtual world interaction networks. The problem is formulated as one of link prediction, intranet work and internet work, in social networks. We use two datasets to study the problem - SOE’s Ever quest II MMO game dataset and IBM’s Small Blue 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.
@inproceedings{borbora2011exploration,title={Exploration of robust features of trust across multiple social networks},author={Borbora, Zoheb H and Ahmad, Muhammad Aurangzeb and Haigh, Karen Zita and Srivastava, Jaideep and Wen, Zhen},booktitle={2011 Fifth IEEE Conference on Self-Adaptive and Self-Organizing Systems Workshops},pages={27--32},year={2011},organization={IEEE},}
