Profile reputation scoring to address trustworthiness in social networks
Karma3 Labs (K3L) implemented a set of strategies to help the Lens Protocol community with a heuristics that can help reveal engaging profiles and recommend interesting content based on heuristics borne from the PageRank algorithm, called EigenTrust co-authored by one of K3L's co-founders, Sep Kamvar.
These strategies are highly intensive to compute using with involvement of linear algebra and matrix convergence to generate EigenValue scores from any graph-like dataset, such as Web3 social graphs from the Lens Protocol ecosystem.
Web3 Social: Lens Protocol Strategies
The following strategies below are used for Lens Protocol's API offered by K3L. All of the strategies will be seeded by 10 profiles chosen as a starting point of hand-picked profiles to begin the computation of trustworthiness. The profiles are:
This strategy emphasizes only on the relevant and meaningful follows as peer-to-peer attestations, disregarding mirrors and comments. If the profile quietly collects NFTs by influencers and creators, these are a signal of non-Sybil activities.
Weight Assignments: Follows = 1
This strategy emphasizes on social engagements as attestations, combining follows, mirrors and comments. The more engagements a profile receives for their posts and profiles, this will result in higher profile scores.
Similar to the engagement strategy, combining follows, mirrors and comments interactions (or attestations) between profiles, but adds another datapoint where posts can be turned into NFT collections by influencers. When these NFTs are collected by others, these are strong signals of a reputable profile.
Similar to the influencer strategy, we add another datapoint where NFT collections that carry a price tag. These become another strong indicator where an influencer has gained a strong following that NFT mints of posts reflect popular amongst a fan base in a creator economy.