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Fuwanovel "Like" Stats


Flutterz

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We've all had someone "like" one of our posts, but have you ever wondered if anyone has ever, like, like-like "liked" your post? Well, now you can find out thanks to this handy-dandy .csv file I made after a couple of hours of coding and waiting for the programs to hunt down every single like on Fuwanovel. Admittedly this only has about 99% of the likes because some of them are either on hidden boards or from members whose accounts have been deleted, but I think that's good enough.

https://docs.google.com/spreadsheets/d/1SLtJc-1Hoq_w9e498zgi9j1f_-8QMyQBPbE4aNK2-Bk/edit?usp=sharing

Now, the next step is to analyze and graph the data, but I need to sleep and then go to work, and even once that's done I haven't done any proper data analysis in a couple of years, so if anyone wants to get started without me, feel free.  ^_^

 

Most likes given

Liker, likee, number of likes given

1292-lewycool, 343-emi, 199
1165-kaguya, 1165-kaguya, 124
8503-eclipsed, 2063-nosebleed, 115
5338-nahichun, 2302-ouraibaa-hjyuraa, 93
7313-stray-cat, 2063-nosebleed, 88
12010-saintofvoid, 10269-xreaper, 87
7101-liquidshu, 2063-nosebleed, 74
8503-eclipsed, 4522-ceris, 71
6569-monmon, 7184-hmn, 68
1310-zenophilious, 2196-flutterz, 65

 
Most mutually beneficial relationships

Liker 1, liker 2, number of likes given in both directions
1292-lewycool, 343-emi, 208
7184-hmn, 6569-monmon, 125
1165-kaguya, 1165-kaguya, 124
8503-eclipsed, 2063-nosebleed, 115
5338-nahichun, 2302-ouraibaa-hjyuraa, 97
7313-stray-cat, 2063-nosebleed, 91
12010-saintofvoid, 10269-xreaper, 89
4522-ceris, 8503-eclipsed, 89
221-tay, 1500-rooke, 84
695-solidbatman, 343-emi, 83

 

Least noticed by their senpai

Liker 1, liker 2, the difference between the likes given "liker 1->liker 2" and "liker 2->liker 1"

1292-lewycool, 343-emi, 190
8503-eclipsed, 2063-nosebleed, 115
5338-nahichun, 2302-ouraibaa-hjyuraa, 89
7313-stray-cat, 2063-nosebleed, 85
12010-saintofvoid, 10269-xreaper, 85
7101-liquidshu, 2063-nosebleed, 74
9913-tyrael, 2063-nosebleed, 63
3519-krill, 2063-nosebleed, 61
5935-aldred, 440-astro, 57
1310-zenophilious, 2196-flutterz, 55

 
Senpai noticed me (a little)

Liker 1, liker 2, liker 1->liker 2(str), liker 2->liker 1(rev), str-rev/max(str,rev) (basically what percentage of the likes in both directions the person that gave more likes has, with all the 100%'s removed, the higher the percentage the more one-sided the likes are)
3519-krill, 2063-nosebleed, 62, 1, 98.39%
5935-aldred, 440-astro, 58, 1, 98.28%
5854-tiagofvarela, 2063-nosebleed, 49, 1, 97.96%
6256-funyarinpa, 2063-nosebleed, 44, 1, 97.73%
4522-ceris, 2063-nosebleed, 44, 1, 97.73%
12010-saintofvoid, 10269-xreaper, 87, 2, 97.70%
6887-rose, 2063-nosebleed, 37, 1, 97.30%
1500-rooke, 2063-nosebleed, 33, 1, 96.97%
7101-liquidshu, 2196-flutterz, 32, 1, 96.88%
7313-stray-cat, 2063-nosebleed, 88, 3, 96.59%

 

Equivalent exchange

Same fields as last time, but limited to those who have at least 30 likes in both directions and sorted in reverse, to find the pairs with the most similar number of likes given

2196-flutterz, 5213-originalren, 17, 17, 0.00%
8503-eclipsed, 221-tay, 19, 18, 5.26%
6569-monmon, 7607-cofee, 17, 16,  5.88%
7156-palas, 1500-rooke, 16, 15, 6.25%
6569-monmon, 7184-hmn, 68, 57, 16.18%
7313-stray-cat, 10870-linovaa, 18, 15, 16.67%
221-tay, 2196-flutterz, 18, 15, 16.67%
8503-eclipsed, 8659-texasdice, 17, 14, 17.65%
7156-palas, 2726-helvetica-standard, 20, 16, 20.00%
2063-nosebleed, 2196-flutterz, 19, 15, 21.05%

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Edit:

 

Statistics is always fun. 

 

Of all my Likes, Eclipsed Monopolizes 10 of the 58 likes, or just over 17%

f89dc761a0.png

 

 

This is how I know Eclipsed loves me: Even in the massive 864 likes he has given, I'm still visible:

d5465bbcd4.png

 

Conclusion: I clearly need to whore myself out more for more likes from Eclipsed

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Now, the next step is to analyze and graph the data, but I need to sleep and then go to work, and even once that's done I haven't done any proper data analysis in a couple of years, so if anyone wants to get started without me, feel free.  ^_^

I'm going to steal your fun and do this before you Fufufufufufu.

 

Incoming edits when I finish.

 

Average number of likes per liked post: 1.543

 

Most liked people:

  1. Nosebleed: 2470
  2. OriginalRen: 1173
  3. Flutterz: 1125
  4. SolidBatman: 956
  5. Ouraibaa-Hjyuraa: 942
  6. Tay: 849 (only included because its Tay)

Most generous likers:

  1. Stray Cat: 1013
  2. Eclipsed: 864
  3. Tiago: 854
  4. LiquidShu: 804
  5. Krill: 755
  6. Tay: 656 (Surprisingly only included because its Tay)

Like Karma: Likes Given - Likes Received, a measure of who whores the most likes if you will.

Like Karma, Top 5:

  1. LiquidShu: +684
  2. Krill: +539
  3. Suzu Fanatic: +320
  4. Silvz: +316
  5. Stray Cat: +315

Like Karma, Bottom 5:

  1. Nosebleed -2252
  2. Original Ren: -945
  3. Ouraibaa: -884
  4. Flutterz: -832
  5. Down: -523

From this point on, Nosebleed becomes a massive outlier, so he got removed from all the charts, otherwise they would be kind of silly.

 

Likes Given vs Likes received:

0623ab0ac7.png

and now on a log scale which sort of cleans things up a bit, as they tend to do with erratic data sets:

5df1b87665.jpg

 

 

Likes per day:

634f804a47.jpg

Someone else can find the retention rate of users and compare it to this, this data set and excel makes it a pain to find them.  Like it makes my head hurt trying to even come up with an efficient way to sort through this data to do it.

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Tch. Now people will realize it's not worth whoring themselves to me to get likes back.

It's about ethics in liking pratices anyway.

 

I'd be interested in seeing some per thread analysis, like say, the Love Live thread for example :sachi:

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Tch. Now people will realize it's not worth whoring themselves to me to get likes back.

It's about ethics in liking pratices anyway.

 

I'd be interested in seeing some per thread analysis, like say, the Love Live thread for example :sachi:

Go bug Flutterz to add that to his program.  Unless there is a formula to calculate which post belongs to which thread (I'm nearly positive there isn't), then with the current data set it's impossible.

 

While I continue to steal Flutterz's fun, give me more ideas on what to parse out and graph and I'll post them here.

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Since he's crawling everything anyways he might as well add thread information to the CSV.

 

Though this would be way easier to do on the database, but alas, that one is locked down.  :sachi:

I realized I should have done that as I went to sleep. Fear not though, for I shall do it once I'm back from work! Might even ask for a shorter day to do it (that and I'm super tired from having gone to sleep way too late two days in a row working on this). I'll also do some quality of life stuff like split usernames and user ids so that it's easier to sort by one of them and change make the like IDs globally unique.

 

 

I'm going to steal your fun and do this before you Fufufufufufu.

 

Incoming edits when I finish.

 

Average number of likes per liked post: 1.543

 

Most liked people:

  1. Nosebleed: 2470
  2. OriginalRen: 1173
  3. Flutterz: 1125
  4. SolidBatman: 956
  5. Ouraibaa-Hjyuraa: 942
  6. Tay: 849 (only included because its Tay)

Most generous likers:

  1. Stray Cat: 1013
  2. Eclipsed: 864
  3. Tiago: 854
  4. LiquidShu: 804
  5. Krill: 755
  6. Tay: 656 (Surprisingly only included because its Tay)

Like Karma: Likes Received - Likes Given, a measure of who whores the most likes if you will.

Like Karma, Top 5:

  1. LiquidShu: +684
  2. Krill: +539
  3. Suzu Fanatic: +320
  4. Silvz: +316
  5. Stray Cat: +315

Like Karma, Bottom 5:

  1. Nosebleed -2252
  2. Original Ren: -945
  3. Ouraibaa: -884
  4. Flutterz: -832
  5. Down: -523

From this point on, Nosebleed becomes a massive outlier, so he got removed from all the charts, otherwise they would be kind of silly.

 

Likes Given vs Likes received:

0623ab0ac7.png

and now on a log scale which sort of cleans things up a bit, as they tend to do with erratic data sets:

5df1b87665.jpg

 

 

Likes per day:

634f804a47.jpg

Someone else can find the retention rate of users and compare it to this, this data set and excel makes it a pain to find them.  Like it makes my head hurt trying to even come up with an efficient way to sort through this data to do it.

I think your karma is Likes Given - Likes Received, not the other way around. No way in hell did Nosebleed give out 2k more likes than he has.

 

 

Go bug Flutterz to add that to his program.  Unless there is a formula to calculate which post belongs to which thread (I'm nearly positive there isn't), then with the current data set it's impossible.

 

While I continue to steal Flutterz's fun, give me more ideas on what to parse out and graph and I'll post them here.

Nah, there's no formula. Every post simply gets an ordered ID, and there's no way (that I've found) to look up posts by ID, which is what made me realize that I really should have added a link to the post, which would consequently keep track of what thread it's from.

 

Nope, I have some ideas but I'm doing them myself. :sachi: Unless you figure them out yourself, in which case >Well Played!

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I realized I should have done that as I went to sleep. Fear not though, for I shall do it once I'm back from work! Might even ask for a shorter day to do it (that and I'm super tired from having gone to sleep way too late two days in a row working on this). I'll also do some quality of life stuff like split usernames and user ids so that it's easier to sort by one of them and change make the like IDs globally unique.

 

 

I think your karma is Likes Given - Likes Received, not the other way around. No way in hell did Nosebleed give out 2k more likes than he has.

The user ID is incredibly easy to parse out in excel though, so don't worry about it.

=right([id-name],(len([id-name])-(find("-",[id-name]))))

 

and fixed, yes you are correct.

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The user ID is incredibly easy to parse out in excel though, so don't worry about it.

=right([id-name],(len([id-name])-(find("-",[id-name]))))

and fixed, yes you are correct.

I'll need to do it anyway. Since you've already done some of the more basic graphs, I can just do the ones that I want and then start writing a program which would allow you to quickly search the data and get basic stats on the user/post/topic etc and it'll more than likely come in handy there.

I'm thinking of sorting everything in an edge graph with users being nodes, that should make getting individual user stats a breeze.

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