Creating the most interesting feed for each user is a complex task and either requires a large amount of data or a new innovative method. Phaver envisions that together with our community we can achieve this task in a novel way - similar to some of the latest wave innovative approaches in web2 social, such as, average video viewing time as input. We believe it is possible by rewarding users for the right things, allowing them as much control as possible - and aligning everyone’s interests around actual interest (pun intended) instead of click baits, surface-level engagement, and mere addictiveness.
What to follow? Each piece of content can be shared inside a topic making it easier to subscribe to specific areas of interest. You can also follow a user to see everything they post. Both actions will help to recommend other relevant topics and subscriptions to customize each user's feed
How to find something specific? To find a specific person or topic you can use the search in the Discovery tab of the app. Each user has a profile with all their posts and other information while each topic has a page of its own.
How to ensure the content is actually good? In popular topics, the votes by others affect the order of content. Specifically, our Post Staking feature gives each Level 2+ user 5-20 daily Stakes to cast on their favorite posts. This helps Phaver to identify high-quality content and reward the best content creators and curators (stakers and moderators).
We are also rolling out also decentralized moderation setup where qualified users can participate in a consensus vote on posts in the mod queue: each voter stakes X points to cast a vote and if all 3 randomized voters agree, each one gets rewarded with 2X points back.
Soon posters who are staking tokens on their own post are also shown with a higher priority since they are putting their tokens as collateral for the quality of their post. Abusers get flagged and experience a decrease in points as a result, while genuine posters get rewarded with more tokens.
Users' Cred level and frens / verified badges also factor into the default visibility in the system. Additionally, the EigenTrust score is being rolled out currently. Phaver is gradually also introducing user-specific machine-learning -based input. As per the ethos of the platform, however, it can also be turned off by the user to be able to see the raw feed and give back control.