Topic: What character is drawn the most each year on this website?

Posted under General

it is interesting to note that if you included only non-safe images Judy and Nick would both fall way down the list since they each have a very high ratio of safe images compared to other characters. while all the ponies had around around 35-40% safe images and the overall average for other popular characters is ~20%, we have Judy with 54.1% of posts being rated safe and Nick having a massive 66.4% of posts rated safe.

and in addition, despite being 6th and 9th overall by quite a large margin, if we only included non-safe images, lucario and renamon actually would come in first and second; having a much smaller 16.3% and 9.5% of posts rated safe respectively.

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Any further questions?

Ponies dominated for 8 years even after the furry art crazes of FNAF and Undertale and especially Zootopia 💀 It took 10 years for the old classics of Sonic and Star Fox to return.

Despite how much Loona has been topping charts, at least there's a variety of franchises on the list now lol

refresh said:
Oh yes, how did you get this data?

import sys import e6csv from e6csv import TagCategory f = open(sys.argv[1], "r") chartags=set() for tag in e6csv.readtags(f): if tag['category'] == TagCategory.CHARACTER: chartags.add(tag['name']) f.close() chartags.difference_update(["fan_character","webcomic_character"]) years=[] f = open(sys.argv[2], "r") for post in e6csv.readposts(f): if post['is_deleted']: continue year = post['created_at'].year - 2007 while year >= len(years): years.append({}) tc = years[year] for tag in post['tag_string']: if tag not in chartags: continue if (n := tc.get(tag)) != None: tc[tag] = n + 1 else: tc[tag] = 0 f.close() for year, tc in enumerate(years): print("h4. "+str(year+2007)) print() topchars = sorted(tc.items(), key=lambda p: p[1], reverse=True)[:10] for tag, count in topchars: print("* [["+tag+"]] ("+str(count)+")") print()

Holy moly, E6 took OFF in 2011! But, unfortunately, this makes me a bit of a normie because I started visiting here around 2012... For mlp porn lmao

sammy301 said:
Holy moly, E6 took OFF in 2011! But, unfortunately, this makes me a bit of a normie because I started visiting here around 2012... For mlp porn lmao

Came for the ponies, stayed for the furries

I couldn't help myself and made a longer version. This time it tracks what happened to every character that featured in the top 10 at least once over the years. I also fixed that off-by-1 error in the previous list's post counts that nobody noticed, and downloaded the newest DB export instead of the one from August I happened to have lying around. Finally, I filtered out that one entry for pokemon_trainer because it was annoying me, which turns out to mean Link gets added to the list.

Long post

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The "(old)" marker indicates that a character had no posts in the previous year, but had had some in one of the years before that. This proved unexpectedly fascinating at times. For example, did you know that the first Judy Hopps image was posted in 2013, and the second in 2015?

there isn't enough zootopia porn on here

Also, LOL @ Sonic being in 11th place no fewer than three times during the Pony Years.

I think I'd be interested in seeing something similar to that YT video but for just pokémon.

also, I feel like this is a focus on characters so often, I think for me the most interesting data would be a look at some of the tag groups within the general category, like a graph that compares all the gender or form tags or maybe just post ratings. it be interesting to cross-reference the effect that stuff like the equestrian explosion had on the population of the feral and probably gynomorph tags. as well as see how the popularity of intersex characters has shifted over time in general, I've kinda had a feeling that the popularity of andromorph characters has been gaining momentum as of the past few years but it's been difficult to quantify without the technical know-how to create a program to do all the data allocation for me.

sipothac said:
I think I'd be interested in seeing something similar to that YT video but for just pokémon.

also, I feel like this is a focus on characters so often, I think for me the most interesting data would be a look at some of the tag groups within the general category, like a graph that compares all the gender or form tags or maybe just post ratings. it be interesting to cross-reference the effect that stuff like the equestrian explosion had on the population of the feral and probably gynomorph tags. as well as see how the popularity of intersex characters has shifted over time in general, I've kinda had a feeling that the popularity of andromorph characters has been gaining momentum as of the past few years but it's been difficult to quantify without the technical know-how to create a program to do all the data allocation for me.

It would be nice if you could just plug in tags you wanted to see and get a timelapse from it. Surprisingly, there isn't much portal_mask stff on e6 given covid unless it's just not tagged

sipothac said:
I think I'd be interested in seeing something similar to that YT video but for just pokémon.

You mean like topic #33499? There's a limit to how useful yearly data can be for Pokémon, since 100 new ones get added every 3 years. In general, species which were once popular seem to remain popular. The only notable distortions come from Gen 1 species with outsized normie hype like Charizard and Pikachu, both of which are now suffering in the ranks.

sipothac said:
also, I feel like this is a focus on characters so often, I think for me the most interesting data would be a look at some of the tag groups within the general category, like a graph that compares all the gender or form tags or maybe just post ratings. it be interesting to cross-reference the effect that stuff like the equestrian explosion had on the population of the feral and probably gynomorph tags. as well as see how the popularity of intersex characters has shifted over time in general, I've kinda had a feeling that the popularity of andromorph characters has been gaining momentum as of the past few years but it's been difficult to quantify without the technical know-how to create a program to do all the data allocation for me.

Genders by year

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