ABSTRACT
We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.
- T. Antal, P. Krapivsky, and S. Redner. Social balance on networks. Physica D, 224(130), 2006.Google Scholar
- M. J. Brzozowski, T. Hogg, and G. Szabó. Friends and foes: ideological social networking. In Proc. 26th CHI, 2008. Google ScholarDigital Library
- M. Burke and R. Kraut. Mopping up: Modeling wikipedia promotion decisions. In Proc. CSCW, 2008. Google ScholarDigital Library
- D. Cartwright and F. Harary. Structure balance: A generalization of Heider's theory. Psych. Rev., 63, 1956.Google Scholar
- M. Chudnovsky, P. Seymour, and B. D. Sullivan. Cycles in dense digraphs. Combinatorica, 28(1):1--18, 2008. Google ScholarDigital Library
- D. Cosley, D. Frankowski, S. B. Kiesler, L. G. Terveen, and J. Riedl. How oversight improves member-maintained communities. In Proc. 23rd CHI, pages 11--20, 2005. Google ScholarDigital Library
- J. A. Davis. Structural balance, mechanical solidarity, and interpersonal relations. Am. J. Soc., 68:444--462, 1963.Google ScholarCross Ref
- R. V. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In Proc. 13th WWW, 2004. Google ScholarDigital Library
- V. Guruswami, R. Manokaran, and P. Raghavendra. Beating the random ordering is hard: Inapproximability of maximum acyclic subgraph. In Proc. 49th IEEE FOCS, 2008. Google ScholarDigital Library
- Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with trustrank. In VLDB '04, 2004. Google ScholarDigital Library
- F. Heider. Attitudes and cognitive organization. J. Psych., 21:107--112, 1946.Google ScholarCross Ref
- S. D. Kamvar, M. T. Schlosser, and H. G. Molina. The eigentrust algorithm for reputation management in p2p networks. In Proc. 12th WWW, pages 640--651. ACM, 2003. Google ScholarDigital Library
- J. Kunegis, A. Lommatzsch, and C. Bauckhage. The Slashdot Zoo: Mining a social network with negative edges. In Proc. 18th WWW, pages 741--750, 2009. Google ScholarDigital Library
- C. Lampe, E. Johnston, and P. Resnick. Follow the reader: filtering comments on slashdot. In Proc. 25th CHI, 2007. Google ScholarDigital Library
- J. Leskovec, D. Huttenlocher, and J. Kleinberg. Signed networks in social media. In Proc. 28th CHI, 2010. Google ScholarDigital Library
- D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. J. Amer. Soc. Inf. Sci. and Tech., 58(7):1019--1031, 2007. Google ScholarDigital Library
- S. Marvel, S. Strogatz, and J. Kleinberg. Energy landscape of social balance. Physical Review Letters, 103, 2009.Google Scholar
- P. Massa and P. Avesani. Controversial users demand local trust metrics: an experimental study on epinions.com community. In AAAI '05, pages 121--126. AAAI Press, 2005. Google ScholarDigital Library
- M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45:167--256, 2003.Google ScholarDigital Library
- B. Pang and L. Lee. Opinion Mining and Sentinment Analysis. Number 2(1--2) in Foundations and Trends in Information Retrieval. Now Publishers, 2008. Google ScholarDigital Library
- P. Resnick and H. R. Varian. Recommender systems. Comm. ACM, 40(3):56--58, 1997. Google ScholarDigital Library
- M. Richardson, R. Agrawal, and P. Domingos. Trust management for the semantic web. In Intl. Semantic Web Conference, 2003.Google ScholarDigital Library
- S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge Univ. Pr., 1994.Google ScholarCross Ref
- F. Wu and B. A. Huberman. How public opinion forms. In Proc. 4th WINE, pages 334--341, 2008. Google ScholarDigital Library
- L. Xiong and L. Liu. Peertrust: supporting reputation-based trust for peer-to-peer electronic communities. IEEE Trans. Knowl. Data Engr., 16(7):843--857, 2004. Google ScholarDigital Library
Index Terms
- Predicting positive and negative links in online social networks
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