Men Who Tip Camgirls: Survey Results

Higher tippers like what sorts of camgirls more?
A: Girls who complain about freeloaders
B: Girls who talk loudly about feminism
C: Girls who look really girl next door
D: Begging for tips on social media
E: ALL OF THE FUCKING ABOVE?!

I started out with 951 responses. I’m interested in motivations of common tippers, so I threw out women tippers and people who said they never watched camgirls. I did, however, leave in two attack helicopters and a deep-dish pizzakin.
genderlist.png
This left me with a sample size of 850 responses.

I grouped some answer sets that had low count, but not very many. Nearly all categories have over 40 responses.

I used DataHero because I’m a complete newb.

If you want more details, check out the camgirl survey

ONTO THE JIZZ

DataHero Age and Watch Rate2.png
I apologize for the graph order.
The older the (bottom range of the) age group, the more frequently they watch camgirls.

DataHero Age and Tipping.png
The older the man, the more he’s likely to tip. This makes a lot of sense, young men are poor. Thanks Obama!

DataHero Tips and Camgirl Fidelity.png
This is fascinating and I don’t know what it means exactly. What the hell is that 2-3 girl range doing so low?

DataHero Freeloader Complaints and Tip amounts.png
The question was, do you have higher approval of girls who express disapproval of freeloaders (7) or girls who don’t (1)?

The more likely men are to approve of a camgirl complaining about freeloaders, the more they are likely to tip. This is interesting and kind of contradicts the camgirl graph from the sister study reporting slightly lower income the more likely they are to complain about freeloaders.

DataHero Room Complaints and Tip Amounts.png
Again, 1 is ‘less likely to tip’ and 7 is ‘more likely to tip.

The question was about camgirls expressing disappointment or displeasure in her room counts or tip amounts being low. I am absolutely astonished to see higher tippers rating this as increasing the likelihood that they will tip her. Of course, maybe they aren’t being honest with themselves, but who knows? I will ask this question in my next camgirl survey to see their side.

DataHero Asking for Tips and Tippers.png
What is this crazytown?? Men more likely to view “begging camgirls” (camgirls who ask for passive tips through non-live means, such as social media) favorably are also bigger tippers. I mean maybe this is obvious to some of you but it’s blowing my mind.

DataHero Camgirl Respect Attempts and Tipping.png
The more a camgirl verbally demands respect, such as saying ‘camgirls deserve respect!’, the more high tippers view her favorably.

I’m starting to think high tippers aren’t the kind of people I thought they were. I had this image of high tippers as world-weary sorts of people, who had plowed through a thousand camgirls and weren’t easily manipulated. I think this idea comes from the sense of powerlessness camgirls frequently have in regards to finding big tippers, and the idea that if she only figures out the ‘secret code’ or something that big tippers want, then they will come to her.

This is painting a different picture. It seems to indicate that high tippers are only special in that they tend to be predisposed (maybe?) to view money-based cam behaviors favorably, or that they have a higher than expected tolerance to camgirls making demands about their jobs and those who watch them (e.g., fuck freeloaders!).

Is this a status thing? That girls who portray a sense of entitlement also seem to be harder to get, and thus men try harder to get them?

I’m really not sure though, definitely open to different ideas on this!

DataHero Indirect Camgirl Respect Attempts and Tipping.png

Weirdly, “indirect respect” attempts (such as emphasizing behind-the-scenes skill it takes to do her job) is more polarizing. The answers on both ends are a bit lower in number though (around 30-40 responses for 1 and 7), does this have something to do with it?

DataHero Feminist Vocalness and Tip Amount.png
The more a man tips, the more likely he is to rate vocal feminism favorably, with a dropoff at the extreme.

DataHero Alternative Aesthetic and Tips.png

This holds up strongly with the camgirl side of the results, too. The more alternative a girl’s style, the less money she makes. Here we see that people who really like alternative styles tend to tip less.

DataHero Traditional Aesthetic and Tips.png

In hindsight I phrased this a bit poorly, but the general idea stands I think. The more ‘girl next door’ a woman looks, the more men report tipping her. The spike around the 3 is interesting, maybe corresponding to the little spike at the 7 on the previous graph? There was a low sample count for that 3, so it might be just high variance.

Overall I would read out of this that polarizing looks (very traditional/aesthetic) do better than middling looks, and that very traditional looks do better than very aesthetic looks.

DataHero Name Announcement and Tip Amount.png
High tippers tend to like the far ends of the spectrum here too, but in general, announcing the name seems like a nicer option.

DataHero Crying on Cam.png
Men who tip a lot are more likely to rate crying camgirls favorably. WHAT IS THIS FUCKING CRAZY WORLD WE LIVE IN

DataHero Room Count and Tip.png

Okay, finally something I understand. The higher the room count of the girl (options were a range of 30 to 300 count), the more likely higher tippers are to clear 10% of the girl’s countdown.

DataHero Girl Getting Tipped and Tip.png

Same question, except instead of low and high room count, it’s low and high tip amount.

The more a man tips, the more likely he is to say he will tip a girl getting few tips instead of many tips.

i dont understand

i don’t understand at all

——–

I have emerged from this more confused than when I started. High tippers report viewing more favorably girls who complain about freeloaders, complain about their room, and who demand respect – all generally behaviors I previously thought were turnoffs.

High tippers also apparently like high room counts with low tips?

ARE HIGH TIPPERS JUST LYING TO US???

This has raised a billion more questions than it answered. More surveys to come after I uncomfortably mull this over.

Have a bonus graph about how high tippers are more likely to date camgirls who refuse to quit camming: DataHero Dating Camgirls and Tips.png

Categories Fun

What makes a camgirl successful? Survey results

Option A: Having natural hair color
Option B: Being really hot
Option C: Doing lots of drugs
Option D: Doing a fuckton of games
Option E: All of the above

hint: the answer is E

My Data Has Limits, Beware!

I got 311 responses from people who identified as cam performers. I threw out all male cammers, people who hadn’t cammed in the last 6 months, people who had wildly inconsistent answers, and people who skipped a lot of questions.

This left me with a sample size of 278.

If there weren’t a lot of answers in a category, sometimes I grouped them together. For example, when asked to rate their own attractiveness, only a handful gave answers spread around the range of 1-5. I combined all of them to function as 5. So, for example, “15% of girls are 5 or under, 22% are 6, 18% are 7, etc.” I don’t know if this is the right way to handle data, so if I made an error here, let me know!

I tried to ensure all categories had at least 25 responses, but most have over 30.

All spectrum answers were out of 7 (e.g., on a scale from 1 (low) and 7 (high), rate your body weight)

I think the error margin for my answers is 5-7%, based off of some light googling, but the margin is probably much higher for correlations. Try to squint when you look at the graphs.

I used DataHero, a correlation finder for dummies complete idiots.

Also please remember my sample size is camgirls who are involved in networking! I used twitter and forums to spread the survey, so I missed camgirls who are disconnected from the community, and their numbers might be very different.

Onto the juice:

DataHero Untitled.png

When I asked about income, I asked for ranges, and plugged in the numbers as the bottom of that range. For example: an income of $1-10 I registered as ‘1.’ The number you see is the average sum of the bottom of all the ranges. The highest category is $200+/hr.

So, out of camgirls who struggle with anxiety/depression frequently, the bottom range of their earnings is $45/hr. Girls who don’t struggle make a bottom range of $62/hr. I don’t actually know if this is the right way to categorize the data, but at least the comparisons between categories seem legit for now.

The total average bottom-range number of all responses is $49 per hour.

DataHero Over last 6 mon, have you struggled with anxietydepression.png

Remember that correlation does not equal causation! It might be that girls who make more money end up being financially secure which leads to less depression. It might also be that girls who are prone to depression have this affect their work life, thus leading to lower income. I don’t know which one it is. Maybe/probably both.

Income and age:DataHero Income and age
I would guess that newer girls tend to be younger, and as thus have a less established base and less income. The income increases once the base is established, but drops off once get too ripe.

DataHero Age and Length of Cam Career
This seems to hold somewhat true. The 27-32 and the 33+ year old categories each generally have been camming for the same amount of time, but those over 33 make substantially less. The 23-26 year olds have cammed for less time than the 27+ age group, but make more money.

DataHero Length of Cam Career and Income.png

If you’ll notice I fucked up a bit when asking about length of camming. If you’ll also notice, there’s no difference in income between girls who have cammed 0-6 months and girls who’ve cammed 6-12 months. 2 years is where it really starts to take off. I don’t know about that dip in the 3rd year.

So far it looks like the secret to success is “start camming really young” and “cam for a long time.”

DataHero Body Weight.png
(Due to low answers, I combined people who answered “1” or “2” into just “2”, and same for 6-7)

Looks like 3/7 bodyweight earns the most – with an interesting spike at the heavier end. Is this a sign of niche preference for fluffier ladies?

DataHero Is camming your only source of income.png
This one is pretty obvious. Remember we don’t know which causes which – the income or the time put in!

DataHero Hours Per Week and Income.png
Turns out the ‘0-5′ and the ’40+’ categories have only around 20 responses each, so expect higher variance there.
That being said, all ways I looked at the data showed a spike around 10-20 hours a week, and 40+ hours a week. Is this indicative of two different types of successful camgirl strategies?

DataHero Days per week and income (1).png
I took out the ‘0-1’ category because there were few responses, but the average reported income for 0-1 days was very low. I say this because I don’t understand why 2 days a week is so high.

DataHero Hours and Days.png
That being said, the hours and days correlation is beautifully strong.

But it looks like there’s a bit of two sweet spots here – working 2 days a week, or 10-15 hours, and working 40+ hours a week, or 5 days a week.
There wasn’t enough data to look closely at the distinctions of hours ‘more’ than 40 a week, but I would guess it falls off at the upper ends, much like days of week falls off once you work over 5 days a week.

Remember: correlation does not equal causation. Working more than 5 days a week does not mean you will make less per hour – it’s very possible that 7-day girls are also ones who work from studios, or split-cam, or something, and thus bring down the income numbers. I don’t know.

DataHero Attractiveness and Income.png
(I combined responses in 1-5 and 9-10 due to low counts)
And maybe the obvious thing we all want to ignore – hotter (at least self-reported hotter) girls make more money. A 6/10 girl will make, on average, a whopping $33 less per hour than a 9/10 girl.

Of course it’s possible girls who think they are hotter are more confident, and confidence is what earns more money. I personally doubt this, however.

DataHero Predicted rank and Actual Income.png

For this question, I asked girls to rank themselves in comparison to other camgirls (for income), and then compared it to the actual income ranking.

There might be something fucky going on with the way I organized the data, but from this it looks like girls who rated themselves “3” or “4” (out of 7) in comparison to other camgirls are overrating themselves. You 3 and 4 girls, you’re doing worse than you think!

DataHero Hair Color and Income.png
Blonde and Brunette competes for the goal, while ‘Other’ lags behind. (grey was an option, but there were so few responders that I filtered that out.) There’s a pretty significant difference in income, with ‘other’ hair colors earning $24 less per hour.

I thought that maybe less attractive people tend to dye their hair weird colors, so I looked at the correlation between hair color and self-rated attractiveness. There was no significant correlation (the biggest difference was 7.26 at black hair, and 7.52 at blonde hair, which I don’t think is a huge difference? ‘Other’ was 7.45, anyway).

DataHero Sexy Shows and Income
Here, “1” was “no sexualness” and “7” was “very explicit. I interpret this as “non-nude” models doing ok, and “kinda sexy girls” doing ok, with everyone else failing for some reason. I really don’t understand that huge difference between 3 and 4.

DataHero Alcohol and Drugs and Income.png

The question was “Do you drink or do other drugs specifically to assist with cam performance or coping with camming?”

I thought maybe this is due to correlation with camming time – girls who cam for a long time eventually turn to drugs or alcohol to cope/help. I was right!
DataHero Cam Career Length and DrugAlcohol use.png
DataHero Member Communication and Income.png
I merged ‘no’ (very few responses) into ‘rarely.’
And, as is unsurprising, the more girls talk to their members off cam, the higher their income.

DataHero Freeloader Complaints and Income.png
This is the question that started it all! I wanted to know if girls who vocalize their disapproval of freeloaders tend to make more or less money. Girls who say ‘no’ or ‘rarely’ make more money than girls who say ‘frequently’ or ‘occasionally’ – though frequently makes more money than occasionally. I don’t know what that’s about.

DataHero Games and Income.png

Here, ‘1’ was low on the “how much do you do games” scale, and ‘7’ was high.

This is really interesting. Girls who say they are 7 on the scale of games do way better than everyone else.

DataHero Site and Income.png
Interestingly, Chaturbate cammers do worse than ‘other.’ Unsurprisingly, MFC girls rake in the big bucks.

DataHero Number of Sites and Income.png
Girls who use 1 site make $28 more per hour than girls who use 2.

DataHero Top 3 Tippers and Income.png
The question was, what percentage of your income comes from your top 3 tippers?
(each answer was a range; ’90’ on the graph was ’90-100%’ range in the answer selection)

DataHero Vocalizing Complaints and Income.png
The question was about whether girls vocalize their complaints about slow days. The results weren’t strong and it appears as though this doesn’t have any significant effect on income.

DataHero Relationship Status and Income.png
Girls who pretend they are single make $18/hr more than girls who admit they aren’t.
However girls who don’t have a SO at all make even less. Most probably, men are less likely to tip girls who they know are dating someone. However a few things:

Girls who have jealous SOs might be more open about them, and jealous SOs might be less supportive of camming in other areas.
Girls with supportive SOs might put less pressure on them to disclose their relationship.
Girls who don’t have any SOs might have much less help in camming overall.

Although – are less attractive girls less likely to date? Let’s check.
DataHero Relationship Status and Attractiveness.png
Nope! No correlation to attractiveness (biggest difference is 0.12).
I suspect this indicates that SOs provide a great deal of behind-the-scenes assistance and motivation.

It’s also possible that girls without SOs also tend to have fewer household expenses, and thus need to make less money to support themselves, and so take camming less seriously.

I don’t think I had enough data to make good predictions about age and relationship status, but it’s possible older women still camming are more likely to be single, and older women make less money.

DataHero Physical Sex and Income.png

“For pay” category had low response number, so don’t take it too seriously.
That being said, 22.4% of girls reported having sexual contact with their members, 15% of it voluntary. Girls who have had voluntary sexual contact with their members make more money. I think this is just that girls who cam longer both tend to make more and tend to eventually become more likely to sex a member. I checked – girls who haven’t sex’d a member have been camming on average 2.55 years, and those who ‘have’ voluntarily sex’d a member have cammed on average 3.62

DataHero Niche Cammers and Income.png

Had low-ish (25) numbers for ‘yes, very much’ so take it with a grain of salt.

DataHero Aesthetic Style and Income.png
Here, 1 was ‘very alternative, tattoos, piercings, etc.’ and 7 was ‘very traditional; no piercings, long hair, etc.’

Generally speaking, the more traditional a camgirl looks, the higher her income.

DataHero Thinking About Work and Income.png
0-4 were combined due to low answer volume.
Looks like girls who either don’t take their work home with them, or do, make the most.

DataHero Parents and Length of Cam Career.png
I initially did this as correlation between parents and income, but then I figured it’s probably more just about ‘how long have you been camming,’ and I think I was right. The longer a camgirl has been camming, the more likely it is that their parents know.

Since the graph cuts it off – the first ‘yes” is “mostly accepting,” and the second “yes” is “mostly disapproving.”

And, as a last bonus: non-nude models (both strictly and loosely, so probably including ‘teasy’ models) make only $4 less per hour than nude models!

So in summary: Start camming early. Be young. Have cammed a lot. Work either really hard or kinda hard, but nowhere in between. Be traditional. Don’t have weird colored hair. Do drugs and drink. Don’t have anxiety. Cam on MFC (only) and do a ton of games. Be super hot. Talk to your members offline. Have sex with your members. Get a boyfriend but don’t tell anyone about it. Don’t be too graphically sexy. Be kinda skinny but not too skinny.

And whalah, you have the recipe (or a description, at least) of a successful camgirl!

If you’re interested in taking more surveys, all currently open ones are under the ‘surveys’ tab above, and I will tweet about new ones I add. This survey has taught me a lot about what things to avoid in survey making, and hopefully the next one will be a lot more accurate, fine-tuned, and useful!

Thank you everyone for your help!

Play Anger

I wish there were a word for anger you don’t believe in.

I mean shallow anger, anger you ‘buy into’ like it’s part of a game. Anger you know would go away if you stopped for a minute to look at the source, but you feel it anyway because it’s fun, because it makes you feel like you’re symbolically supporting some sort of ideology that agrees with that anger.

I feel this anger when horny men message me really stupid things, like “can u send me a pic of ur butthole”. I have no actual right to be angry. I put nudes of myself on the internet, I welcome sexual comments, and I am completely unsurprised by horny men sending me horny messages. Of course. I understand. Deep down I am calm.

But on a surface-pretend level I think lots of terrible insults at them and think of myself as an empowered woman whose body is sacred and powerful, so powerful not just anybody can look at her butthole, _especially_ not people who sends her grammatically offensive tweets from an avatar of a penis.

I also feel this with okcupid profiles when I see people say they’re a feminist. I am a bit skeptical of feminism but I can understand how a rational person would agree with it, and it means different things to different people, and I’m open to discussion.

But every single fucking goddamn okcupid profile aggressively mentions feminism, usually in the first few paragraphs. What do you think you’re doing?? Everyone in your white college-educated town who isn’t turned off by you already is going to also be a feminist. It isn’t brave, it’s unoriginal. All you’re doing is signalling. I want to hit your stupid conformist face.

When I stop and breathe, I know I don’t actually think that. Smart people mention feminism on their profiles. People I like. They have reasons for it. I understand. Deep down I am calm.

I still need a word for the surface anger, though. For now I’m going to call it playnger but if any of you come up with a more clever term I’d love to hear it.

Categories Fun

Conversational Styles

I’ve been viewing social interactions lately through two spectrums – word count and conversational deference.

Word count is simple – the total amount of words peoples say, and if it’s high or low. Some people talk a lot, others don’t talk very much.

Deference is the amount of dominance you use when speaking. When two people start to talk at the same time, whoever lets the other person go is displaying deference. Someone who interrupts is displaying nondeference. Allowing someone to interrupt is displaying deference. Continuing to speak despite indications that the listener would like to chime in is displaying nondeference.

So there are then four categories, and because I have a guilty pleasure of personality tests and fun identity words, here’s my attempt to name the four styles.

High word count | high deference: The Catalyst.
People like this I view as conversationally meaty; they propel conversation forward, fill silences, but easily step back once other people want to participate. They are catalytic, and provide a steady background for the rest of the conversation to take place. When done poorly it can sound like nervous jabber, when done well it draws other people out without pressure or obligation.

High word count | low deference: The Elbow.
People like this I view as conversationally aggressive and forceful (whether they’re aware of it or not). They treat conversation as a service to them and their ideas. When done poorly it’s annoying and pushy, when done well it’s useful in leadership situations and for authoritatively directing the flow of attention to something better.

Low word count | high deference: The Wallpaper.
People like this I view as shy or thoughtful; will typically not speak much and not try to speak much if they feel like people don’t want to listen. They treat conversation distantly, as something they usually aren’t heavily involved in. Can be a pushover or introverted or both. When done poorly it’s indicative of insecurity, when done well it can provide a service of listening and attention that many people crave.

Low word count | low deference: The James Bond. 
People who speak rarely but expect to be listened to; treats conversation as generally unworthy or boring, and only selectively determines it as worth their time. When done poorly it can come across as a pretentious arrogant superiority complex, done well it can be mysterious, dominant, and charismatic.

Categories Fun

Loyalty Hierarchies

I like thinking of proper secret-sharing protocol in the context of loyalty hierarchies.

When I share personal information with someone, I have an implicit assumption that they will share this information only with people higher in their loyalty hierarchy than I am.

For example: I tell my friend Barbara that I am having marriage difficulty and I am worried my husband Bob is going to divorce me. I would consider it inappropriate if she shared this information down-rank with our casual coworker Beth, but it would be fine if she shared it up-rank, with her childhood best friend of 25 years Brittany, or Barbara’s husband Billy.

As in; every time I choose to share information, I assume I am sharing it with a tree of loyalty. I do not just share information with Barbara, I am sharing it with Barbara-Brittany-Billy.

A loyalty branch ends when information is shared to you by your closest loyal partner. If Barbara’s highest loyal rank – her husband – shares personal information with her, there is nobody up-rank to spread the information to, so the chain dies with her.

I am only displeased by information sharing when it is shared down-rank. Sharing up-rank means that the information will end soon – the end being whenever it is shared with the highest loyal rank (husband to Barbara). If everyone shares up-rank, the information spread is contained. But in spreading down and up-rank, the spread can go indefinitely, until everybody in the world knows and is telling me to divorce Bob already and get it over with.

Of course this is negated if explicitly stated otherwise. If I tell Barbara not to tell a soul about my troubled relationship with Bob, then I would expect her not to.

I don’t know if other people operate by this rule or not, but I get the impression that most people vaguely adhere to it in general terms. Do you have any sorts of rules for privacy and information sharing?

Categories Fun

Wiki Links

Over time I’ve been saving some of my favorite articles from those inevitable Wikipedia doom spirals. Here’s a list of odd, interesting, unusual, or curious links straight from my juicy bookmarks folder!

Fatal familial insomnia

Stigmata

Common cuckoo #Breeding

Mudita

Shutdown law

List of selfie-related injuries and deaths

HIV/AIDS denialism

Wright brothers patent war

Igaluk

Anula of Anuradhapura

Mass hysteria

Blonde versus brunette rivalry

Shotgun house

Dead man’s switch

Morbid jealousy

Black Death Jewish persecutions

Spite house

Work spouse

Privilege du blanc

Orkney

Picts

Norman conquest of England #Origins

Adrian Schoolcraft

Lists of unsolved problems

List of cities by population density

Tarrare

H. H. Holmes

Opir

List of helicopter prison escapes

Perverse incentive

Congo Free State #Mutilation

Pataphysics #Concepts

Mill Ends Park

Garden hermit

Republic of Molossia

Project MKUltra

Liberia #Early settlement

Varangian Guard

The Miracle of 1511

Phaistos Disc

Lithopedion

Toynbee tiles

Gobekli Tepe

Backmasking #Satanic backmasking

Kim Philby

Tower of Wooden Pallets

Animal suicide

Empress Dowager Hu #As Emperor Xuanwu.27s concubine

White elephant  #Examples of alleged white elephant projects

Psychopomp

Nuliajuk

Pope Benedict IX

Late Bronze Age collapse

1918 flu pandemic

Sweater curse

Ching Shih

1% rule (Internet culture)

Five Punishments

Franklin’s lost expedition

Rosenhan experiment

Form constant

Moral panic

St Scholastica Day riot

Carnac stones

 

Categories Fun

Kinks and Kinkiness

I recently did a kink survey, where I had you guys rate how kinky (in the sense of taboo or socially scandalous) they thought various kinks were.

I picked 31 different kinks, fetishes, preferences, and types of play. I grouped together some kinks in the same family (monsters/tentacles, diaper/infantilism, etc.). A lot of people either misread or didn’t read my instructions at all, and so I unfortunately had to throw out around 15% of the results. Lesson learned – I’ll be clearer next time!

I ended up with a total of 443 usable responses, with 121 female and 322 male. (I eliminated the ‘other’ category because there were only 12 usable answers)

So without further ado, here is the Official Kink Rating:

  • 1.38: Sex positions (doggystyle, 69ing, etc.)
  • 2.37: Uniforms (costumes; police, maid, etc.)
  • 2.42:  Spanking
  • 2.68: Light Bondage (fuzzy handcuffs, silk blindfolds, etc.)
  • 2.7:  Anal sex
  • 4.08: Sex outside (at work, in public bathrooms, in nature)
  •  4.2:  Latex
  • 4.32: Voyeurism
  • 4.33: Dominance/submission
  • 4.46: Exhibitionism
  • 5.21: Masochism (arousal from receiving pain)
  • 5.29: Transformations (from smart to bimbo, growing muscles, limbs, inflation, etc.)
  • 5.8:  Sadism: (arousal from giving pain)
  • 5.74: Lactation (breast milk)
  • 5.81: Inanimate objects (attraction to shoes, panties, buildings)
  • 5.93: Asphyxiation (choking self or others)
  • 6.16: Futa (girls with dicks)
  • 6.32: Heavy bondage (full immobility, suspension, predicament bondage, etc.)
  • 6.59: Monsters (tentacles, aliens, deformities, etc.)
  • 6.62: Rapeplay
  • 6.82: Watersports (urination)
  • 7.16: Dirtiness (soiled things, decaying things, disgust)
  • 7.25: Piercing/cutting (the act of piercing)
  • 7.8:  Incest
  • 8.01: Diaper/infantile (or any other form of child roleplay)
  • 8.34: Insects (including other creepy crawlies, seafood, etc.)
  • 8.61: Vore (being consumed or consuming another person/creature)
  • 8.78: Bestiality
  • 9.05: Pedophilia (I am aware many do not consider this a kink please stop messaging me)
  • 9.07: Scat (poop)
  • 9.51: Necrophilia (sexual attraction to dead bodies)

Men rated kinkiness on average at a 5.86, while women rated on average of 6.0

There were some differences in gender for individual kinks. Here are the top 7 discrepancies.

Voyeurism (.8)

Men: 4.1 Women: 4.9

Lactation (.6)

Men: 5.6 Women: 6.2

Dirtiness (.6)

Men: 7 Women: 7.6

Futa (.6)

Men: 6.4 Women: 5.8

Inanimate Objects (.5)

Men: 5.7 Women: 6.2

Diaper/infantile (.5)

Men: 7.9 Women: 8.4

Light bondage (.4)

Men: 2.8 Women: 2.4

Followups, all at .3, are incest, bestiality, monsters, creepy crawlies, and sadism, all which women think are more kinky.

The two that men consider more kinky is futa and light bondage, while they think all the others are less kinky than women do. I’m not really sure how to interpret this. My first thought was that people were rating according to perceived backlash, so for example men think futa is kinkier because society might mock them for being gay, but that doesn’t explain things like voyeurism, which has a worse stigma against peeping men, but is rated less kinky by men.

I also asked people to count how many of the listed kinks did turn them on (a good test being – have they or would they have searched for porn of it – or does it make your penis hard/vagina wet?).

Men: 10
Women: 8.5

so men on average liked about 1.5 more items on the list than women did. This might because I subconsciously listed more male-friendly kinks? I’m not sure. I initially thought it was too woman-friendly, because I am a woman and a lot of kinks I thought of were kinks that I had. But who knows. So I added up the score of all the kinks I have and got a total rounded sum of 68.

Is that a lot? I don’t know. Add up your total and reblog this post with your kink score!

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