Profile reputation scoring to address trustworthiness in social networks
The Eigentrust algorithm for reputation management in P2P networks
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MeritRank: Sybil Tolerant Reputation for Merit-based Tokenomics*
Decentralized reputation systems are emerging as promising mechanisms to enhance the effectiveness of token-based economies. Unlike traditional monetary incentives, these systems reward participants based on the actual value of their contributions to the network. However, the advantages and challenges associated with such systems remain largely unexplored. In this work, we investigate the inherent trade-offs in designing a decentralized reputation system that is simultaneously generalizable, trustless, and Sybil-resistant. Specifically, “generalizable” means that the system can assess various types of contributions across different contexts, “trustless” indicates that it functions without the need for a central authority to oversee reputations, and “Sybil-resistant” refers to its ability to withstand manipulations by fake identities, i.e., Sybil attacks.
(PDF) The EigenTrust Algorithm for Reputation Management in P2P Networks
PDF | Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent... | Find, read and cite all the research you need on ResearchGate
OpenRank (Karma3) — Onchain Atlas
Karma3 Labs' decentralized reputation-compute protocol that ran EigenTrust over onchain and social graphs to power verifiable rankings, feeds, and sybil-resistant airdrops — technically validated but shut down in 2026 for lack of a business model.
EigenTrust
The EigenTrust algorithm is a reputation management algorithm for peer-to-peer networks, developed by Sep Kamvar, Mario Schlosser, and Hector Garcia-Molina.[1] The algorithm provides each peer in the network a unique global trust value based on the peer's history of uploads and thus aims to reduce the number of inauthentic files in a P2P network. It has been cited by approximately 5800 other articles according to Google Scholar.[2]
Reranking partisan animosity in algorithmic social media feeds alters affective polarization
Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field ...
Jesse Ruderman » The Advogato trust metric is not attack-resistant
The project for the graduate algorithms class I took in Fall was to compare two algorithms for a problem of our choice. I chose to compare two trust metrics, the Advogato trust metric and Google's PageRank. Trust metrics attempt to solve the following problem: "Given a directed graph where an edge from A to B means 'A trusts B', whom should I trust?".
(PDF) HonestPeer: An Enhanced EigenTrust Algorithm for Reputation Management in P2P Systems
PDF | The visible success of the Peer to Peer (P2P) paradigm is associated with many challenges in finding trustworthy peers as reliable communication... | Find, read and cite all the research you need on ResearchGate
The Importance of Validation and Decay In Community Reputation Metrics
When measuring contributions in your community, here are some essential recommendations for building fairness, retention, and growth.
Scaling trust on the web
The Task Force for a Trustworthy Future Web's report on gaps and opportunities for how the next generation of online spaces will be built.
Models for Trust Inference in Social Networks
Interpersonal trust between any two people in social networks is hard to gauge, and even harder to infer, given that these two people are not connected by an immediate social link, such as friendship or acquaintanceship. In order to be able to make accurate inferences for an arbitrary tuple of people in a given social environment, we present an approach, named Appleseed, that is based on mechanics taken from neuropsychology, known as spreading activation models. Compelling in its simplicity, we relate the concept to trust propagation and evaluation in an intuitive fashion. While Appleseed works very well when paths between two arbitrary people in the network can be established, no inference of trust is possible when this is not the case. To this end, we present several algorithms for inferring trust that go beyond network structure and demonstrate their accuracy in real social networks. We also show how these algorithms can be augmented with additional data that may be available in some contexts.
Elena Rossini 🌈 (@_elena@mastodon.social)
SCOOP: #WSocial is doctoring metrics on its homepage, inflating the number of comments on posts by prominent people on its network. I suppose a more accurate tagline for them should be "Trust your feed?" My article about it: "W Social, Fictional Metrics and the Beauty of Open Data" 🔗 : https://blog.elenarossini.com/w-social-fictional-metrics-and-the-beauty-of-open-data/ #blog #BigTech #EUBigTech #TEP #TrustedEuropeanPlatforms #TrustYourFeed
W Don't Have Time | Offprint
"Trust community, not wealth" Holding power accountable on the open social web.
Social Media Feed Ranking Algorithms: Guide to Field Experiments
IC2S2'26 Tutorial | Social Media Feed Ranking Algorithms: Guide to Field Experiments
Bridging-Based Ranking
There is significant concern about the engagement-based ranking systems used by TikTok, Facebook, YouTube, etc. to recommend content. Bridging-based ranking systems can address one of the most dangerous aspects of such algorithmic recommendations—the push toward polarization and divisiveness that is tearing nations apart—and do so without reducing anonymity or increasing censorship. This report explores what bridging-based ranking is, how it helps (overcoming downsides of chronological feeds and middleware), addresses common objections, and provides early examples of its use and benefits in the wild. The report concludes by providing next steps for platforms, governments, funders, and researchers in order to accelerate the deployment of bridging.
