Requirements
- [X] Is this a feature request? For questions or discussions use https://lemmy.ml/c/lemmy_support
- [X] Did you check to see if this issue already exists?
- [X] Is this only a feature request? Do not put multiple feature requests in one issue.
- [X] Is this a backend issue? Use the lemmy-ui repo for UI / frontend issues.
Is your proposal related to a problem?
Yes, the current “Top” sorting algorithms on Lemmy prioritize votes, which sometimes results in posts with thousands of votes and only a few comments appearing at the top. This can be frustrating as it may not accurately reflect user engagement. Additionally, sorting by “Most Comments” disregards votes completely, which is not ideal either. A balanced sorting algorithm that combines various metrics is needed to better represent user engagement and prevent manipulation through bot accounts.
Describe the solution you’d like.
I propose that Lemmy incorporates a balanced sorting algorithm that considers multiple user engagement metrics, such as the number of comments, votes, time spent on a post, etc. For example, a “Balanced” sorting algorithm that considers 50% upvotes, 30% comments, and 20% time spent reading. By combining these factors, the sorting algorithm would better represent the interests of the community and be less susceptible to manipulation.
Describe alternatives you’ve considered.
An alternative solution could be to implement a moderation system that detects and prevents vote manipulation, similar to Feature Upvote’s approach. However, this may not address the issue of accurately representing user engagement in the sorting algorithm.
Additional context
User engagement is a crucial aspect of any online platform, as it indicates the success and relevance of the content. Implementing a balanced sorting algorithm that combines various user engagement metrics would not only improve the overall user experience on Lemmy but also make the platform more resistant to manipulation. This approach would address the issue of posts with only a few comments appearing on the feed and provide a more accurate representation of user engagement on the platform.
