15 years ago a Scorolli et al. (2007) looked at the relative prevalence of fetishes online.
This study has since been cited numerous times, and continue to be cited to this day.
But, the world has changed a lot since then! The AI sex robot industry have actually started selling robots in high volumes, and sex tech in general have revolutionized the possible fetishes.
In 2007, Scorolli et. al. looked at online communities in forums which brought with it some crucial limitations:
- It neglected online communities on social media
- It only allowed for the study to produce findings on the relative prevalence of different fetishes, not the absolute prevalence in the public.
In sum, 15 years has passed with techtonic shifts in sexual desires and on top of this new breakthrough technologies are available for data aggregation and analysis.
On top of this the 2007 studies proved to have some limiting factors.
Therefore, we decided (a mere 6 months ago) to replicate and improve on this study with new 2022 data.
The research took 6 months and we aggregated over 655,341 datapoints from 844 online communities on both social media and online forums. We estimate over 112,000 individuals where incorporated in the study.
Additionally we conducted a nationally representative survey study of 7,533 respondents to provide data on the absolute prevalence of different fetishes.
Key statistics on fetishes
- Foot fetish is the most prevalent fetish of all (11% of the population have a foot fetish) – more popular than cuckolding (8%), voyeurism (5%) and age play (3%).
- 18% of men have a foot fetish, while only 4% of women have a fetish for feet and toes.
- Age play is the most popular fetish amongst women (6% of women).
- Twice as many have a fetish for voyeurism (watching others) compared to exhibitionism (exposing yourself for others).
- Cuckolding/cuckqueaning (fantasizing of an adulterous partner) is over 20 times more prevalent amongst men compared to women.
- Women fetishize anime/hentai more than men.
The results of our systematic repetition of the study by Scorolli et al. (2007) showed very similar results. However, new categories have been introduced, as our search basis expanded.
Additionally the table below also contains survey data on the absolute prevalance of different fetishes in our nationally representative study.
For further data access please du contact: firstname.lastname@example.org
|Fetish||Sexological classification||Relative Prevalence||Absolute Prevalence (men, women)|
|Feet, toes||Podophilia, foot fetish||36%||11% (18%, 4%)|
|Adulterous partner||Cuckolding (for men), cuckqueaning (for women)||22%||8% (15%, <1%)|
|Watching others (typically undress or during sex)||Voyeurism||12%||5% (6%, 4%)|
|Age play||Infantilism, DDLG (daddy dom little girl)||8%||3% (<1%, 6%)|
|Public exposure (naked or having sex)||Exhibitionism||7%||3% (4%, 2%)|
|Anime and Hentai||Ecchi, Omorashi||5%||2% (1%, 2%)|
|Body fluids (blood, urine, etc.)||Golden/brown showers, watersport, urophilia, scatophilia, lactaphilia, menophilia, mucophilia||3%||1% (1%, <1%)|
|Body size (obesity, tall, short, etc.)||Chubby chasers, nanophilia||2%||1% (ns, ns)|
|Muscles||Cratophilia (strength), sthenophilia (muscle)||2%||<1% (ns, <1%)|
|Dressing and acting as animals||Anthropomorphic, furries||2%||<1% (ns, ns)|
|Body modifications (tattoes, pierceing, etc.)||Tattoing, piercing, ringing, stigmatophilia||1%||<1% (ns, ns)|
|Belly or navel||Alvinophilia||<1%||<1% (ns, ns)|
|Ethnicity||Allotriorastry, miscegenation, xenophilia||<1%||<1% (ns, ns)|
|Breasts||Mammaphilia, mammagynophilia, mastofact||<1%||<1% (<1%, ns)|
|Legs, buttocks||Crurofact, Pygophilia||<1%||<1% (ns, ns)|
|Mouth, lips, teeth||Odontophilia||<1%||<1% (ns, ns)|
|Body hair||Hirsutophilia, gynephilus- and pubephilia (pubic hair fetish), depilation||<1%||<1% (ns, ns)|
|Body odor||Mysophilia, osmophilia||<1%||<1% (ns, ns)|
Foot fetish is the most prevalent fetish
The research showed similar data on the prevalence of foot fetishes compared to other types of fetishes.
Foot fetish is the most prevalent fetish relative to all other fetishes measured on how many participants and activity can be measured in online communities. In other words:
- 36% of online activity around different fetishes is related too foot fetish.
The same picture formed when we measured the prevalence of foot fetish in our nationally representative survey:
- 11% of the population indicate that they have a fetish for feet and toes (18% men, 4% women)
Cuckolding and Cuckqueaning
The second most predominant fetish recorded in our study is the fetish of an adulterous partner.
For men this fetish is called cuckolding, while for women it is called cuckqueaning.
Our study showed that:
- 22% of online groups and chats on fetishes revolve around cuckolding and cuckqueaning.
- 8% answer they have a cuckolding fetish when asked, making it the second most popular fetish.
Exhibitionism and Voyeurism
Collectively exhibitionists and voyeurists make up over 8% of the population.
- 5% of the population report to be a voyeurist.
- 3% of the population report to be an exhibitionist.
Data source: For the relative prevalence of different fetishes we used the exact same methodology as Scorolli et al. (2007), however, the data collection was expanded to include a multitude of social media groups and pages on Facebook, Twitter, Instagram, WhatsApp, and Telegram. These groups where searched and found through a rigorous process of searching different fetishes on each platform. Secondly, for the survey we used MTurk, as that have been shown to provide results similar to a nationally representative sample. The survey was designed as an open-ended survey and conducted in a fashion were respondents could report to have multiple fetishes, and additionally report any unmentioned fetishes. A strict fetish definition was presented to each respondent before they started the survey, so that no respondent would belive fantasizing.
Data analysis: We initially identified 933 online communities based on our initial search. Each group was then qualitatively coded in an open-ended fashion, and then reconfirmed by a third-party. After the open-ended coding of each group we excluded 89 groups from the data pool as we coded them as a group related to a non-sexual obsession rather than a sexual fetish. After this we identified 17 predominant fetish categories from the open-ended coding. After this we recoded each group into one of the 17 categories. Less than 3% of the groups could be attributed to one or more categories. These were then coded in the category that was predominant in the group messages. This leads to the second stage, were we used a scraper to count 1) the number of members in each group, and secondly, 2) count the number of messages sent in each group. This gave a summative index of the level of activity that we calculated the relative prevalence on the basis of.