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Robust features of trust in social networks

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Abstract

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.

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Notes

  1. http://en.wikipedia.org/wiki/EverQuest_II.

  2. In case of the EverQuest II dataset, we use player sums of session lengths (in minutes) to approximate avatar age. A player session is defined as a contiguous period of player activity. Since the activity logs in EverQuest II only record player actions, we used a simple heuristic to define player session. A session consists of sets of activities which are separated by no more than 30 min.

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Acknowledgments

The research reported herein was supported by the AFRL via Contract No. FA8650-10-C-7010, the ARL Network Science CTA via BBN TECH/W911NF-09-2-0053 and by DARPA via Grant Number W911NF-12-C-0028. The data used for this research were provided by the Sony Online Entertainment. We gratefully acknowledge all our sponsors. We would also like to thank Nishith Pathak for his valuable critique and feedback while writing the paper. The findings presented do not in any way represent, either directly or through implication, the policies of these organizations.

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Authors and Affiliations

  1. Department of Computer Science, University of Minnesota, Minneapolis, MN, 55455, USA

    Zoheb Hassan Borbora, Muhammad Aurangzeb Ahmad, Jehwan Oh & Jaideep Srivastava

  2. Raytheon BBN Technologies, 5775 Wayzata Blvd, St., Louis Park, MN, 55416, USA

    Karen Zita Haigh

  3. IBM Thomas J. Watson Research Center, Hawthorne, NY, 10532, USA

    Zhen Wen

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  1. Zoheb Hassan Borbora
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Correspondence to Zoheb Hassan Borbora.

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Borbora, Z.H., Ahmad, M.A., Oh, J. et al. Robust features of trust in social networks. Soc. Netw. Anal. Min. 3, 981–999 (2013). https://doi.org/10.1007/s13278-013-0136-6

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