Excess reciprocity distorts reputation in online social networks

The peer-to-peer (P2P) economy relies on establishing trust in distributed networked systems, where the reliability of a user is assessed through digital peer-review processes that aggregate ratings into reputation scores. Here we present evidence of a network effect which biases digital reputation, revealing that P2P networks display exceedingly high levels of reciprocity. In fact, these are much higher than those compatible with a null assumption that preserves the empirically observed level of agreement between all pairs of nodes, and rather close to the highest levels structurally compatible with the networks’ reputation landscape. This indicates that the crowdsourcing process underpinning digital reputation can be significantly distorted by the attempt of users to mutually boost reputation, or to retaliate, through the exchange of ratings. We uncover that the least active users are predominantly responsible for such reciprocity-induced bias, and that this fact can be exploited to obtain more reliable reputation estimates. Our findings are robust across different P2P platforms, including both cases where ratings are used to vote on the content produced by users and to vote on user profiles.

[1]  Chrysanthos Dellarocas,et al.  The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias , 2006, Manag. Sci..

[2]  Giulio Cimini,et al.  How Evolutionary Dynamics Affects Network Reciprocity in Prisoner's Dilemma , 2014, J. Artif. Soc. Soc. Simul..

[3]  Matthew J. Salganik,et al.  Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market , 2006, Science.

[4]  D. Garlaschelli,et al.  Early-warning signals of topological collapse in interbank networks , 2013, Scientific Reports.

[5]  Michael Szell,et al.  Multirelational organization of large-scale social networks in an online world , 2010, Proceedings of the National Academy of Sciences.

[6]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[7]  J. Byers,et al.  The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry , 2017 .

[8]  Steven Tadelis,et al.  The Limits of Reputation in Platform Markets: An Empirical Analysis and Field Experiment , 2015 .

[9]  D. Helbing,et al.  How social influence can undermine the wisdom of crowd effect , 2011, Proceedings of the National Academy of Sciences.

[10]  Gary E. Bolton,et al.  Engineering Trust - Reciprocity in the Production of Reputation Information , 2009, Manag. Sci..

[11]  F. Heider.  ATTITUDES AND COGNITIVE ORGANIZATION , 1977 .

[12]  Rosario N. Mantegna,et al.  Statistically validated mobile communication networks: the evolution of motifs in European and Chinese data , 2014, ArXiv.

[13]  Paul Resnick,et al.  I Scratched Yours: The Prevalence of Reciprocation in Feedback Provision on eBay , 2010 .

[14]  Alessandro Pluchino,et al.  The Beneficial Role of Random Strategies in Social and Financial Systems , 2012, Journal of Statistical Physics.

[15]  Guido Caldarelli,et al.  Scale-Free Networks , 2007 .

[16]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[17]  L. White,et al.  Fussing and Fuming Over Fannie and Freddie: How Much Smoke, How Much Fire? , 2004 .

[18]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[19]  Georgios Zervas,et al.  A first look at online reputation on Airbnb, where every stay is above average , 2015, Marketing Letters.

[20]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[21]  Mantegna,et al.  Noise enhanced stability in an unstable system. , 1996, Physical review letters.

[22]  Chun Liu,et al.  Social Influence Bias : A Randomized Experiment , 2014 .

[23]  Paul A. Pavlou,et al.  Overcoming the J-shaped distribution of product reviews , 2009, CACM.

[24]  Jérôme Kunegis,et al.  KONECT: the Koblenz network collection , 2013, WWW.

[25]  Diego Garlaschelli,et al.  Patterns of link reciprocity in directed networks. , 2004, Physical review letters.

[26]  E. Fehr,et al.  Fairness and Retaliation: The Economics of Reciprocity , 2000, SSRN Electronic Journal.

[27]  Steven Tadelis,et al.  The Economics of Reputation and Feedback Systems in E-Commerce Marketplaces , 2016, IEEE Internet Computing.

[28]  Claudio Altafini,et al.  Consensus Problems on Networks With Antagonistic Interactions , 2013, IEEE Transactions on Automatic Control.

[29]  Sergey N. Dorogovtsev,et al.  Critical phenomena in complex networks , 2007, ArXiv.

[30]  Jure Leskovec,et al.  Signed networks in social media , 2010, CHI.

[31]  Giorgio Fagiolo,et al.  Null models of economic networks: the case of the world trade web , 2011, 1112.2895.

[32]  Vili Lehdonvirta,et al.  Crowdsourcing for Public Policy and Government , 2015 .

[33]  Andrea Capocci,et al.  Degree correlations in signed social networks , 2014, ArXiv.

[34]  Thomas Poell,et al.  Social Media and the Transformation of Public Space , 2015 .

[35]  C. Altafini,et al.  Computing global structural balance in large-scale signed social networks , 2011, Proceedings of the National Academy of Sciences.

[36]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[37]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[38]  M. Jackson,et al.  An Economic Model of Friendship: Homophily, Minorities and Segregation , 2007 .

[39]  P. Resnick,et al.  The value of reputation on eBay: A controlled experiment , 2006 .

[40]  Matthew Pearson,et al.  Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb , 2015, EC.

[41]  N. Taleb.  Antifragile: Things That Gain from Disorder , 2012 .

[42]  Nicola Zannone,et al.  A reference model for reputation systems , 2014, Decis. Support Syst..

[43]  Alessandro Pluchino,et al.  The Peter Principle Revisited: A Computational Study , 2009, Physica A: Statistical Mechanics and its Applications.