Excess reciprocity distorts reputation in online social networks
Open accessAbstract
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.
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All profile elements were created to reflect the general ratings and content observed on Sharing Economy platform profiles to ensure quality and ecological validity.
“To ensure quality and ecological validity, all elements were created to reflect the general ratings and content observed on SE platform profiles [4,47].”
From Digital Identity: The effect of trust and reputation information on user judgement in the Sharing Economy (2018) · cites this paper as [47]
The current "5-for-5" culture in ratings drastically reduces the overall diagnosticity of TRI in favor of social cohesion and community participation.
“As it has been argued [47], the current “5-for-5” culture in ratings drastically reduces the overall diagnosticity of TRI in favor of social cohesion and community participation.”
From Digital Identity: The effect of trust and reputation information on user judgement in the Sharing Economy (2018) · cites this paper as [47]
Ratings on SE platforms show a stronger bias towards high ratings than on other P2P platforms, which severely reduces their usefulness to users.
“Indeed, ratings on SE platforms show a stronger bias towards high ratings than on other P2P platforms [4,47], which severely reduces their usefulness to users.”
From Digital Identity: The effect of trust and reputation information on user judgement in the Sharing Economy (2018) · cites this paper as [47]
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- Authors
- Livan, G., Caccioli, F., & Aste, T.
- Affiliations
- Systemic Risk Centre (GB)
- Corresponding author
- Livan, Giacomo
- Venue
- Scientific Reports
- Published
- 2017-06-14
- Volume
- 7
- Issue
- 1
- Pages
- 3551-3551
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5471239
- OpenAlex
- W2613416984
- Cited by
- 20
- References
- 27
- Citations by year
- 2025: 2 · 2022: 1 · 2021: 4 · 2020: 5 · 2019: 2 · 2018: 6
- FWCI
- 1.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Systemic Risk Centre (ES/K002309/1)
- Engineering and Physical Sciences Research Council (EP/N006062/1, K002309, ES/K002309/1)
- Economic and Social Research Council (ES/K002309/1)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
- Keywords
- reciprocity, online reputation, social networking sites, peer-to-peer networks, trust, rating aggregation, network effects, crowdsourcing, retaliation, user activity, peer review
- MeSH terms
- Commerce, Humans, Models, Statistical, Trust, Social Networking
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