Methods and systems for ranking comments on a post in an online service

1. A method, comprising:

at a server system having one or more processors and memory storing one or more programs configured for execution by the one or more processors:

receiving a plurality of comments about a post on an online service;

receiving feedback, provided by users of the online service, regarding respective comments of the plurality of comments;

retrieving a respective user weight for each user that provided feedback;

for each of the plurality of comments, computing a respective aggregated score of the user weights for users that provided feedback for the respective comment;

ranking the plurality of comments based on the respective aggregated score for each comment; and

providing the plurality of comments, ordered in accordance with the ranking, for display.

2. The method of claim 1 , wherein ranking the plurality of comments comprises:

for a first comment of the plurality of comments, summing the user weights of the users who have provided feedback on the first comment.

3. The method of claim 1 , further comprising, at the server system:

determining the user weights for the users; and

storing the user weights.

4. The method of claim 3 , wherein determining the user weights comprises:

obtaining a collection of user comments from a plurality of users on posts in the online service, wherein respective user comments of the collection have been assigned respective ratings;

determining a correlation between the respective user comments and users who have provided feedback on the respective user comments; and

calculating the user weights based at least in part on the correlation.

5. The method of claim 4 , wherein:

determining the correlation between the respective user comments and users comprises training a machine-learning model using the respective user comments and their assigned respective ratings; and

calculating the user weights comprises applying the machine-learning model to the feedback provided by the users.

6. The method of claim 1 , wherein the feedback comprises indications that users like respective user comments.

7. The method of claim 1 , wherein ranking the plurality of comments comprises:

for a first comment of the plurality of comments, using a maximum user weight of the user weights of the users who provided feedback on the first comment.

8. The method of claim 1 , wherein ranking the plurality of comments comprises:

for a first comment of the plurality of comments, using a minimum user weight of the user weights of the users who provided feedback on the first comment.

9. The method of claim 1 , wherein a respective user has multiple user weights for different categories of subjects.

10. The method of claim 1 , further comprising, at the server system, periodically updating the user weights for the users.

11. The method of claim 1 , further comprising, at the server system, decreasing the user weight of a respective user in response to the respective user providing feedback that is contrary to a trend.

12. The method of claim 1 , wherein:

the user weight of a new user is zero; and

ranking the plurality of comments comprises ignoring feedback from the new user, in accordance with the zero feedback weight.

13. The method of claim 1 , further comprising, at the server system, determining the user weight of a respective user based at least in part on timeliness of feedback from the respective user.

14. The method of claim 1 , further comprising, at the server system, determining the user weight of a respective user based at least in part on historical data indicating whether the respective user's feedback anticipates trends.

15. The method of claim 1 , further comprising, at the server system, determining the user weight of a respective user based at least in part on an age of an account of the respective user on the online service.

16. The method of claim 1 , further comprising, at the server system, determining the user weight of a respective user based at least in part on a correlation between a ranking of a comment and whether feedback provided by the respective user on the comment is positive or negative.

17. The method of claim 16 , wherein determining the user weight of the respective user comprises assigning a negative user weight to the respective user based at least in part on a determination that the respective user provided positive feedback on the comment and that the comment does not satisfy a ranking threshold.

18. The method of claim 16 , wherein determining the user weight of the respective user comprises reducing the user weight of the respective user based at least in part on a determination that the respective user provided positive feedback on the comment and that the comment does not satisfy a ranking threshold.

19. A server system, comprising:

one or more processors; and

memory storing one or more programs configured for execution by the one or more processors, the one or more programs including instructions for:

receiving a plurality of comments on a post in an online service;

receiving feedback, provided by users of the online service, regarding respective comments of the plurality of comments;

retrieving a respective user weight for each user that provided feedback;

for each of the plurality of comments, computing a respective aggregated score of the user weights for users that provided feedback for the respective comment;

ranking the plurality of comments based on the respective aggregated score for each comment; and

displaying the plurality of comments in accordance with the ranking.

20. A non-transitory computer-readable storage medium storing one or more programs configured for execution by one or more processors, the one or more programs including instructions for:

receiving a plurality of comments on a post in an online service;

receiving feedback, provided by users of the online service, regarding respective comments of the plurality of comments;

retrieving a respective user weight for each user that provided feedback;

for each of the plurality of comments, computing a respective aggregated score of the user weights for users that provided feedback for the respective comment;

ranking the plurality of comments based on the respective aggregated score for each comment; and

displaying the plurality of comments in accordance with the ranking.