Predicting positive and negative links in online social networks

WWW, pp. 641-650, 2010.

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Keywords:
social networksocial computingsocial psychology
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By casting this as a problem of sign prediction, we have identified principles that generalize across multiple domains, and which connect to social-psychology theories of balance and status

Abstract:

We study online social networks in which relationships can be ei- ther 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 tha...More

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Introduction
  • Social interaction on the Web involves both positive and negative relationships — people form links to indicate friendship, support, or approval; but they link to signify disapproval of others, or to express disagreement or distrust of the opinions of others.
  • For a given link in a social network, the authors will define its sign to be positive or negative depending on whether it expresses a positive or negative attitude from the generator of the link to the recipient.1.
  • Arbitrary hyperlinks on the Web can be used to indicate agreement or disagreement with the target of the link, though the lack of explicit labeling in this case makes it more difficult to reliably determine this sentiment [20].
Highlights
  • Social interaction on the Web involves both positive and negative relationships — people form links to indicate friendship, support, or approval; but they link to signify disapproval of others, or to express disagreement or distrust of the opinions of others
  • We have investigated some of the underlying mechanisms that determine the signs of links in large social networks where interactions can be both positive and negative
  • By casting this as a problem of sign prediction, we have identified principles that generalize across multiple domains, and which connect to social-psychology theories of balance and status
  • Our methods for sign prediction yield performance that significantly improves on previous approaches
  • A first one is to explore methods that might yield still better performance for the basic sign prediction problem, and to understand whether the features that are relevant to more accurate methods help in the further development of social theories of signed links
  • As noted at the outset, the role of positive and negative relationships in on-line settings is not limited to domains where they are explicitly tagged as such
Results
  • The authors use a logistic regression classifier to combine the evidence from these individual features into an edge sign prediction.
  • Logistic regression learns a model of the form P (+|x) = 1+ 1 e−(b0 +.
  • ) n i bi xi where x is a vector of features (x1, .
  • The edges signs in the networks that the authors study are overwhelmingly positive.
  • The authors use the full dataset where about 80% of the.
  • Predictive accuracy 1 (A) 1 (B) 1 (C)
Conclusion
  • The authors have investigated some of the underlying mechanisms that determine the signs of links in large social networks where interactions can be both positive and negative.
  • By casting this as a problem of sign prediction, the authors have identified principles that generalize across multiple domains, and which connect to social-psychology theories of balance and status.
  • As noted at the outset, the role of positive and negative relationships in on-line settings is not limited to domains where they are explicitly tagged as such
Summary
  • Introduction:

    Social interaction on the Web involves both positive and negative relationships — people form links to indicate friendship, support, or approval; but they link to signify disapproval of others, or to express disagreement or distrust of the opinions of others.
  • For a given link in a social network, the authors will define its sign to be positive or negative depending on whether it expresses a positive or negative attitude from the generator of the link to the recipient.1.
  • Arbitrary hyperlinks on the Web can be used to indicate agreement or disagreement with the target of the link, though the lack of explicit labeling in this case makes it more difficult to reliably determine this sentiment [20].
  • Results:

    The authors use a logistic regression classifier to combine the evidence from these individual features into an edge sign prediction.
  • Logistic regression learns a model of the form P (+|x) = 1+ 1 e−(b0 +.
  • ) n i bi xi where x is a vector of features (x1, .
  • The edges signs in the networks that the authors study are overwhelmingly positive.
  • The authors use the full dataset where about 80% of the.
  • Predictive accuracy 1 (A) 1 (B) 1 (C)
  • Conclusion:

    The authors have investigated some of the underlying mechanisms that determine the signs of links in large social networks where interactions can be both positive and negative.
  • By casting this as a problem of sign prediction, the authors have identified principles that generalize across multiple domains, and which connect to social-psychology theories of balance and status.
  • As noted at the outset, the role of positive and negative relationships in on-line settings is not limited to domains where they are explicitly tagged as such
Tables
  • Table1: Dataset statistics
  • Table2: Logistic regression coefficients compared to status and structural balance theory. × means there is discrepancy in predictions between the Balance (Status) theory and what is learned from the logistic regression model. Each line represents directions and signs of the edges on a path (A, B, C) where “BFpm" stands for Backward Forward plus minus and denotes a path A ←+ B → −C
  • Table3: Regression coefficients based on Balance attributes and learned logistic regression
  • Table4: The coefficients based on Status Theory and learned logistic regression
  • Table5: Learned logistic regression coefficients for the model based on the counts of directed positive paths
  • Table6: Predictive accuracy when training on the “row”
  • Table7: Predictive accuracy when training on the “row” dataset and evaluating the prediction on the “column” dataset
  • Table8: Fraction of edges satisfying global balance and status
  • Table9: Predicting the presence of a positive edge
Download tables as Excel
Related work
  • Earlier in the introduction, we discussed some of the main lines of research on which we are building; here, we survey further lines of study that are also related to our work.

    First, our use of trust networks as a source of data connects to a large body of work on trust management in several settings, including peer-to-peer networks [12, 25], Semantic Web applications [22], and Web spam detection [10]. Related to trust management is the development of user rating mechanisms on sites such as Slashdot [13, 14] and the development of norms to control deviant behavior [6]. Recent work has also investigated online communities devoted to discussion of controversial topics, where one can expect to find strong positive and negative interactions [2, 24]; and the analysis of sentiment, subjectivity, and opinion in text has become an active area in natural language processing [20].

    Our general goal of inferring an individual’s attitudes suggests parallels to a long line of work on recommendation systems [21], in which the goal is typically to infer how a user would evaluate given items based on their evaluation of other items. There are crucial differences, however, between an analysis in which a user is evaluating (inert) items, and our case in which a user is evaluating other people — in this latter case, the objects being evaluated are themselves capable of forming opinions and expressing attitudes, and this provides additional sources of information based on the full social network of interactions.
Funding
  • Research was supported in part by the NSF grant IIS-0705774, IBM Faculty Award, gift from Microsoft Research and Yahoo! Research Alliance grant
Reference
  • T. Antal, P. Krapivsky, and S. Redner. Social balance on networks. Physica D, 224(130), 2006.
    Google ScholarLocate open access versionFindings
  • M. J. Brzozowski, T. Hogg, and G. Szabó. Friends and foes: ideological social networking. In Proc. 26th CHI, 2008.
    Google ScholarLocate open access versionFindings
  • M. Burke and R. Kraut. Mopping up: Modeling wikipedia promotion decisions. In Proc. CSCW, 2008.
    Google ScholarLocate open access versionFindings
  • D. Cartwright and F. Harary. Structure balance: A generalization of Heider’s theory. Psych. Rev., 63, 1956.
    Google ScholarLocate open access versionFindings
  • M. Chudnovsky, P. Seymour, and B. D. Sullivan. Cycles in dense digraphs. Combinatorica, 28(1):1–18, 2008.
    Google ScholarLocate open access versionFindings
  • 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 ScholarLocate open access versionFindings
  • J. A. Davis. Structural balance, mechanical solidarity, and interpersonal relations. Am. J. Soc., 68:444–462, 1963.
    Google ScholarLocate open access versionFindings
  • R. V. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In Proc. 13th WWW, 2004.
    Google ScholarLocate open access versionFindings
  • 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 ScholarLocate open access versionFindings
  • Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with trustrank. In VLDB ’04, 2004.
    Google ScholarLocate open access versionFindings
  • F. Heider. Attitudes and cognitive organization. J. Psych., 21:107–112, 1946.
    Google ScholarLocate open access versionFindings
  • 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 ScholarLocate open access versionFindings
  • 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 ScholarLocate open access versionFindings
  • C. Lampe, E. Johnston, and P. Resnick. Follow the reader: filtering comments on slashdot. In Proc. 25th CHI, 2007.
    Google ScholarLocate open access versionFindings
  • J. Leskovec, D. Huttenlocher, and J. Kleinberg. Signed networks in social media. In Proc. 28th CHI, 2010.
    Google ScholarLocate open access versionFindings
  • 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 ScholarLocate open access versionFindings
  • S. Marvel, S. Strogatz, and J. Kleinberg. Energy landscape of social balance. Physical Review Letters, 103, 2009.
    Google ScholarLocate open access versionFindings
  • 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 ScholarLocate open access versionFindings
  • M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45:167–256, 2003.
    Google ScholarLocate open access versionFindings
  • 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 ScholarLocate open access versionFindings
  • P. Resnick and H. R. Varian. Recommender systems. Comm. ACM, 40(3):56–58, 1997.
    Google ScholarLocate open access versionFindings
  • M. Richardson, R. Agrawal, and P. Domingos. Trust management for the semantic web. In ISWC, 2003.
    Google ScholarFindings
  • S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge Univ. Pr., 1994.
    Google ScholarFindings
  • F. Wu and B. A. Huberman. How public opinion forms. In Proc. 4th WINE, pages 334–341, 2008.
    Google ScholarLocate open access versionFindings
  • 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 ScholarLocate open access versionFindings
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