BFF: A tool for eliciting tie strength and user communities in social networking services

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10796-013-9453-6The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and TIN 2008-04446 and PROMETEO II/2013/019 projects. This article has been developed as a result of a mobility stay funded by the Erasmus Mundus Programme of the European Comission under the Transatlantic Partnership for Excellence in Engineering - TEE Project.López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2014). BFF: A tool for eliciting tie strength and user communities in social networking services. Information Systems Frontiers. 16:225-237. https://doi.org/10.1007/s10796-013-9453-6S22523716Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.Boyd, D., & Hargittai, E. (2010). Facebook privacy settings: who cares? First Monday, 15(8).Burt, R. (1995). Structural holes: the social structure of competition. Harvard University Pr.Culotta, A., Bekkerman, R., McCallum, A. (2004). Extracting social networks and contact information from email and the web.Ellison, N., Steinfield, C., Lampe, C. (2007). The benefits of facebook friends: social capital and college students use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168.Fang, L., & LeFevre, K. (2010). Privacy wizards for social networking sites. In Proceedings of the 19th international conference on World wide web (pp. 351–360). ACM.Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75–174.Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of the 27th international conference on human factors in computing systems (pp. 211–220). ACM.Girvan, M., & Newman, M. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Science, 99(12), 7821.Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 1360–1380.Gross, R., & Acquisti, A. (2005). Information revelation and privacy in online social networks. In Proceedings of the 2005 ACM workshop on privacy in the electronic society (pp. 71–80). ACM.Johnson, M., Egelman, S., Bellovin, S. (2012). Facebook and privacy: it’s complicated. In Proceedings of the eighth symposium on usable privacy and security (p. 9). ACM .Kahanda, I., & Neville, J. (2009). Using transactional information to predict link strength in online social networks. In Proceedings of the third international conference on weblogs and social media (ICWSM).Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: a comparative analysis. Physical Review E, 80, 056–117.Lancichinetti, A., Fortunato, S., Kertsz, J. (2009). Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics, 11(3), 033–015.Lin, N., Ensel, W., Vaughn, J. (1981). Social resources and strength of ties: Structural factors in occupational status attainment. American Sociological Review, 393–405.Lipford, H., Besmer, A., Watson, J. (2008). Understanding privacy settings in facebook with an audience view. In Proceedings of the 1st conference on usability, psychology, and security (pp. 1–8). Berkeley: USENIX Association.Liu, G., Wang, Y., Orgun, M. (2010). Optimal social trust path selection in complex social networks. In Proceedings of the 24th AAAI conference on artificial intelligence (pp. 139–1398). AAAI.Matsuo, Y., Mori, J., Hamasaki, M., Nishimura, T., Takeda, H., Hasida, K., Ishizuka, M. (2007). Polyphonet: an advanced social network extraction system from the web. Web Semantics: Science, Services and Agents on the World Wide Web, 5(4), 262–278. World Wide Web Conference 2006 Semantic Web Track.Murukannaiah, P., & Singh, M. (2011). Platys social: relating shared places and private social circles. Internet Computing IEEE, 99, 1–1.Quercia, D., Lambiotte, R., Kosinski, M., Stillwell, D., Crowcroft, J. (2012). The personality of popular facebook users. In Proceedings of the ACM 2012 conference on computer supported cooperative work (CSCW’12).Rosvall, M., & Bergstrom, C. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4), 1118–1123.Sharma, G., Qiang, Y., Wenjun, S., Qi, L. (2013). Communication in virtual world: Second life and business opportunities. Information Systems Frontiers, 15(4), 677–694.Shen, K., Song, L., Yang, X., Zhang, W. (2010). A hierarchical diffusion algorithm for community detection in social networks. In 2010 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC) (pp. 276–283). IEEE.Sierra, C., & Debenham, J. (2007). The LOGIC negotiation model. In AAMAS ’07: proceedings of the 6th international joint conference on autonomous agents and multiagent systems (pp. 1–8). ACM.Staddon, J., Huffaker, D., Brown, L., Sedley, A. (2012). Are privacy concerns a turn-off?: engagement and privacy in social networks. In Proceedings of the eighth symposium on usable privacy and security (p. 10). ACM.Strater, K., & Lipford, H.R. (2008). Strategies and struggles with privacy in an online social networking community. In Proceedings of the 22nd British HCI group annual conference on people and computers: culture, creativity, interaction, BCS-HCI ’08 (Vol. 1, pp. 111–119). Swinton: British Computer Society.Wellman, B., & Wortley, S. (1990). Different strokes from different folks: Community ties and social support. American Journal of Sociology, 558–588.Wiese, J., Kelley, P., Cranor, L., Dabbish, L., Hong, J., Zimmerman, J. (2011). Are you close with me? are you nearby? investigating social groups, closeness, and willingness to share. In Proceedings of the 13th international conference on Ubiquitous computing (pp. 197–206). ACM.Xiang, R., Neville, J., Rogati, M. (2010). Modeling relationship strength in online social networks. In Proceedings of the 19th international conference on World wide web (pp. 981–990). ACM

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