Content Voting

Unlike certain traditional content distribution models, the ReadON system utilizes a content voting system which allows users and curators to influence which content becomes popular and gets added to a sub-community.

Traditional System A: A Centralized Model using Machine Learning, or "TikTok" Style

In these platforms, all uploaded content is parsed through a centralized model, which then uses machine learning to optimize the click-through rate of a piece of content. Content is shown to users that are most likely to interact with it, which serves the platform’s goal of keeping users’ attention, but also tends to bias users to only interact with content they want to see, creating so-called “information cocoons”. In these platforms, the models and data that power the centralized model are the core assets and are owned by the platform.

Traditional System B: A Subscription Model, or "Twitter/RSS" Style

Here, there is no centralized model as users select which content sources (distributors) to subscribe to. Creators can add content to different distributors and users can subscribe to different distributors. However, users may find it time-consuming to obtain information efficiently as they have to subscribe to each distributor individually. It can also be a lengthy process for new users to find the influencers they like.

ReadON’s solution

ReadON’s solution is focused on enabling and promoting organic content sharing while giving power to the user to control what content becomes popular. To do this, ReadON will create sub-communities based on topics. Experienced users, those who hold Tier 2 Glasses NFTs, of those sub-communities will become curators of the corresponding topics. Curators can cast votes on new content; votes are not visible until the voting phase ends.
Voting Power: Users can stake tokens to get voting power and to cast their votes.
Content Score: Content score is used to rank content within its sub-community, and is computed via the Quadratic Voting Procedure to prevent major token owners from dominating the ranking results.
Voting Outcomes: 10% of all reading rewards generated from a piece of content will be shared by its voters, distributed by their voting power, and another 10% of total reward will go to the creator.
We have proved that the Nash Equilibrium will be reached when all parties tell their true understanding of the content quality.