I'd love to see how well it could predict comment ratings from Hacker News.
The following data would be a good start:
1. Text of comment
2. How many points the comment has
3. How many points the article has
4. Time article was posted
5. Time comment was posted
I'd also be interested to see what kind of user bias there is. If you don't provide user names, you could see what kind of rating a comment should have based on its content, and what rating it actually has because certain users are generally loved (pg) or hated (jasonmcalacanis) by the community.
There should be one more data point in there, and it's the hard part, because it makes the dataset nonlinear:
6. The same data for parent and children of the comment
Every comment's rating is heavily swayed by its position within the thread. A comment replying to something at the bottom won't get voted on. A comment replying to something at the top, with just as much merit, will almost always get somewhere between 0.5x to 1.0x as many votes as its parent. A child comment somewhere within a low-voted comment's descendants saying "why am I getting downvoted?" has the potential to call off the latch-on-downvoting effect, and thus give the comment a chance to spike upward. A comment in reply to something no one bothered reading will be ignored despite its merits. Etc.
Of course, your "content worth rating" is orthogonal to all of those concerns, and would probably help in changing the patterns mentioned above, which are mostly bad for the comment futures market. :)
That would be interesting. Does anyone have a dataset? Ideally to do this type analysis you would also need to know the upvote (or downvote) history of the users so you can pinpoint any bias, or predict which comments you as a user are most fond of reading.
Comments
I'd love to see how well it could predict comment ratings from Hacker News.
The following data would be a good start:
1. Text of comment
2. How many points the comment has
3. How many points the article has
4. Time article was posted
5. Time comment was posted
I'd also be interested to see what kind of user bias there is. If you don't provide user names, you could see what kind of rating a comment should have based on its content, and what rating it actually has because certain users are generally loved (pg) or hated (jasonmcalacanis) by the community.
To be fair, several posters can earn their points on name alone:
- grellas can post a one liner on a law issue
- DarkShikari can post about video codecs
- tptacek can post about security
- patio11 can post about bingo
- edw519 can post a grocery list
There should be one more data point in there, and it's the hard part, because it makes the dataset nonlinear:
6. The same data for parent and children of the comment
Every comment's rating is heavily swayed by its position within the thread. A comment replying to something at the bottom won't get voted on. A comment replying to something at the top, with just as much merit, will almost always get somewhere between 0.5x to 1.0x as many votes as its parent. A child comment somewhere within a low-voted comment's descendants saying "why am I getting downvoted?" has the potential to call off the latch-on-downvoting effect, and thus give the comment a chance to spike upward. A comment in reply to something no one bothered reading will be ignored despite its merits. Etc.
Of course, your "content worth rating" is orthogonal to all of those concerns, and would probably help in changing the patterns mentioned above, which are mostly bad for the comment futures market. :)
That would be interesting. Does anyone have a dataset? Ideally to do this type analysis you would also need to know the upvote (or downvote) history of the users so you can pinpoint any bias, or predict which comments you as a user are most fond of reading.
That sounds really interesting! If anyone does the analysis it would be cool to see the results.