[NERDSHIT] RDrama concept map, and other data :marseychartscatter: :marseyakshually:

Context

For my recent project to add hashtags to automeme, I created a database that contained the strength of the relationship between every pair of tokens that appeared in rdrama comments since August of last year. (Two tokens are said to be "related" if they appear in the same comment)

This allows me to find keywords that might be related to other keywords. For instance, "train" relates to these words (excluding "uninteresting" words) (in order of most to least powerful):

  • trains
  • women
  • trans
  • drama
  • ai
  • twitter
  • kids
  • posts
  • online
  • men
  • dude
  • retarded
  • thread
  • rdrama
  • ones
  • weird
  • crazy
  • funny
  • gay
  • chud
  • reddit

RDrama relates to

  • reddit
  • drama
  • rightoids
  • thread
  • marsey
  • website
  • banned
  • comments
  • internet
  • funny
  • jannies
  • carp
  • twitter
  • fun
  • rightoid
  • sub
  • bait
  • online
  • retarded
  • chud
  • net
  • dot
  • content

Okay, so it isn't perfect. But it is cheap! Running this system lasts only a few seconds (I do a lot of additional processing, including some graph searches for adjacent topics

Most Powerful

The most used (INTERESTING) words are

women - 7291
based - 5466
retarded - 5045
reddit - 5030
true - 4211
rdrama - 4158
funny - 4097
gay - 3836
twitter - 3775
men - 3762
god - 3746
fat - 3672
kids - 3621
fun - 3500
drama - 3368
trans - 3333
dude - 3256
internet - 3227
chud - 3214
foid - 3187
tbh - 3030
marsey - 2958
foids - 2898
unironically - 2806
sounds - 2799
retard - 2720
ones - 2698
cool - 2675
thread - 2673
ass - 2648
days - 2603
tho - 2602
times - 2599
weird - 2588
posts - 2528
stupid - 2385
comments - 2279
carp - 2263

Yes, dramatards are obsessed with women. Straggot alert!

What are the most powerful relationships? Well...

(women, men) 1208
(women, trans) 449
(ukraine, russia) 419
(jesus, christ) 416
(mental, illness) 343
(twink, cute) 281
(harry, potter) 263
(rightoids, leftoids) 253
(russia, russian) 249
(twitter, elon) 243
(ukraine, russian) 238
(men, gay) 236
(kids, parents) 229
(male, female) 229
(trans, rights) 215
(women, male) 214
(foids, foid) 208
(trans, lives) 205
(twitter, reddit) 200
(times, multiple) 195
(women, foids) 195
(foids, moids) 195
(women, fat) 194
(women, gay) 191
(cope, seethe) 191
(sub, bait) 189
(sub, reddit) 188
(reddit, banned) 187
(musk, elon) 184
(chud, award) 183
(rdrama, reddit) 183
(men, foids) 176
(women, female) 174
(reddit, comments) 170
(women, children) 168
(russian, ukrainian) 167
(kids, children) 166
(users, rdrama) 155
(posts, comments) 155
(reddit, subs) 153
(kiwi, farms) 153
(posts, reddit) 153
(tate, andrew) 150
(cute, twinks) 144
(women, foid) 143
(men, trans) 142
(women, attractive) 142
(online, internet) 141
(women, funny) 139
(filter, slur) 137
(youtube, videos) 137
(men, male) 137
(china, russia) 136
(thread, reddit) 135
(women, true) 135
(ai, artists) 133
(alex, jones) 131
(reddit, jannies) 130
(ukraine, ukrainian) 129
(rdrama, user) 129
(god, bless) 128
(chad, virgin) 128
(foid, moid) 128
(kids, women) 128
(women, rape) 128
(democratic, collapse) 127
(heckin, valid) 126
(retarded, women) 126
(women, ones) 125
(ugly, fat) 125

Pretty Graphs

The image above is a graph that links the most common words together. The darker the line connecting them, the more powerful the connection.

Here's a slightly more frantic one, with a lot more nodes...

![](/images/16741036979843237.webp)

Here's one with around a thousand nodes

![](/images/16741053924477208.webp)

Future

The algorithim as I implemented it does not understand n-grams, and I have been brainstorming ways to add n-gram support. Also, a lot of the tokens are the same word in different tenses, so perhaps I could consolidate those tokens.

56
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Nice work. You should check out socnetv - it has a tool for crawling web pages and also a lot of built in node analysis stuff

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that actually looks really cool! I will check it out for sure

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For sure dog. Let me know if you wanna collab on some rdrama analytics. I got a whole gang of cowtools I use. I've been meaning to make some LDA topic models or do some linguistic analysis of this site for a while now :marseynerd2:

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the thing I really wanted to do was relationship modelling based off of who upkongs who, but the urls for getting that information struggle with powerusers. I can't even load the data for mine https://rdrama.net/@HeyMoon/upkongrs. I was talking with sneks to see if there were any ways to improve the efficiency of the call but it is very unlikely, lol, considering the absolutely enormous size of the Votes database

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Yeah, seems like that call is totally broken for powerusers. If I had a list of users I can run something to capture all that resolve/don't resolve (502). Then maybe sneks could run the ones that don't manually?

Edit: maybe what you are looking to do could be accomplished by using

@HeyMoon/upkonging ? Logic being people have probably recieved more votes than they have given. So upkongrs is naturely gonna be longer than upkongd

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