Publication Date

Fall 2023



As part of the California Institute of Integral Studies (CIIS) Human Sexuality Summer Research Fellowship, the Neurological Disability group collected social media data regarding the intersections of neurological disability and sexuality. For the purpose of the database, neurological disability is determined as self-identified neurological disorders and mental health diseases that can be congenital or acquired, including but not limited to: autism, depression, anxiety, traumatic brain injury, etc. (Wade, 1996). Key terminology for neurological disorders included both a review of literature focusing on biomedicalization of terminology and social justice terminology, as well as a practical review of the efficacy of the search terms through a trial and error method on respective platforms (Jette, 2009; Anastasiou et al., 2014; Andrews et al., 2019). The fellows were mindful of language that reflected medical diagnosis, advocacy language, and layman's terms. Importantly, the structure of the fellowship split the overarching topic of disability into neurological disability and physical disability, using their expertise fellows included or excluded data based on the self-declared identities of the social media user, if a post or comment included the mention of multiple disabilities that crossed both physical and neurological, the data was only collected if the primary conversation was in relation to neurological disability. Data that focused more on physical disability was flagged for colleagues on the physical disability team. Key terms regarding sex/sexuality were brought under the umbrella sexual function and practice, however items regarding sexual identity and partnership were captured.

Once terminology was agreed upon, fellows combined one key term from neurological disability with a term from sexuality and modified based on Algospeak inorder to generate data from their respective social media sites, combinations included but were not limited to neurodivergent and sex, ADHD and sex, depression and sex toys. Algospeak, refers to codes or modified words that social media users utilize in order to navigate around safety features on various platforms; fellows were mindful to capture major algospeak guides (i.e. seggs vs sex) in order to effectively find data (Delkic, 2022). The data was organized to capture posts, comments, and key demographic information.

General Information

Neurological Disabilities Data Collection Methods:

Fellows assigned to the neurological disability team were split into data collection groups across four social media platforms: Twitter, Reddit, Youtube, and TikTok. Key terms for the neurological group were determined through a review of literature that covered both the specific aspects of sexual lives of people with disabilities as well as lived experiences of adults with intellectual disabilities (Carew et al., 2017; Gil-Llario et al., 2018; Holdsworth et al., 2018). After having an understanding of the individual, social, and structural level experiences of people with disabilities reviewed in current academic research, terms were generated and tested on individual platforms to test for efficacy. From the individual testing, fellows brought back and agreed upon general terms to use, including but not limited to: disability, persistent mental illness, and neurodivergence. Terms that did not have a critical mass of posts associated with it were removed. Neurological key terms were paired with sexual behavior terms that went through a similar platform specific testing. Sexual behavior terms looked mostly at sexual function, however data regarding gender identity, biological sex, sexuality, and sexual orientation were captured. Fellows only moved on from pairings of key terms once they had exhausted all possible searches or reached saturation on their platform. Data included in the database has explicit discussion of both a neurological disability and pertained to some aspect of sexual function or practice. Excluded from the neurological database were posts that regarded sexual orientation but not sexual behavior/function/practice and posts that discussed sexual assault but did not discuss impact of sexual function.

As posts, comments, and videos were collected, fellows tracked information including key demographics (race/ethnicity, gender, nationality, disability, and other identities) indicating whether they were perceived or declared by the poster, as well as platform specific demographic areas such as pronouns and sexual orientation. All data collected included the key terms combination used in the platform specific search function in order to arrive at the results. Platform specific metrics were also included by each fellow, which were determined in platform working groups (i.e. youtube included video creator, subscribers, likes, etc.). Each fellow collected the text or audio of the post as close to the original as possible, including algo-speak and emojis, in cases where emojis were used but unrepresented fellows wrote in a description of the emoji. During the collection process, assigned data managers reviewed the data and provided feedback on format, collection dates, and overall archive consistency. Issues regarding formatting, if posts were part of exclusionary criteria, and criteria to separate out were discussed in the disability group team meetings and reviewed with the faculty lead on a weekly basis.

Key Terms

Key terminology


  • Disability

  • Persistent Mental Illness

  • Neurodivergent

  • Neurodiversity

  • Neurotypical

  • Cognitive Impairment

  • Cognitive Disability

Sexual Behavior

  • Sexual Dysfunction

  • Emotional intelligence

  • Pleasure

  • sex/ seggs

  • Orgasm

  • Sex toys

  • Porn

  • Disabled and Sexy

  • Disabled and queer

  • Sexual dysfunction

  • Libido

  • Erogenous zone

  • Masturbate (and slang)

  • Penetration

  • Foreplay

  • Intimacy

Specific Illness

  • ADHD

  • Autism

  • AuDHD (Autism and ADHD)

  • ASD (Autism Spectrum Disorder)

  • Bipolar

  • Mania/manic

  • Depression

  • Major Depression

  • Down Syndrome

  • Epilepsy

  • BPD (Borderline Personality Disorder)

  • Schizophrenia

  • Aphasia

  • Memory Loss

  • Stroke

  • Brain Injury (TBI, etc)

  • Complex post traumatic stress disorder (CPTSD)

  • OCD

  • Personality Disorders

  • PTSD

  • Tourette Syndrome

  • Anxiety

  • GAD (Generalized Anxiety Disorder)

  • Dyslexia

  • Intellectual sexual disorder

  • Dyspraxia

Combinations used:

Neurodivergent and sex, autism and sex, disability and libido, disability and sex toys, neurodivergent and libido, depression and sex, autism and sexuality, ADHD and sex, autism and libido, neurodisability and sex, bipolar disorder and sex, OCD and sex, schizophrenia and sex, neurodivergent and masturbation, PTSD and sex, bipolar and sex, cognitive disability and sex

Platform Specific Information

Platform specific use:


To collect data on Reddit, fellows created new Reddit accounts through school emails and utilized an incognito browser to prevent personal algorithms from interfering in search functions. When setting up their new Reddit accounts fellows were asked about new account demographics in which they selected 18+. Reddit requires new accounts to select three required areas of interest and follow at least one subreddit; these were selected in relation to the scope of the fellows’ specific topic area. With the exception of one fellow, all searches for key terms were done within the Reddit site itself using its search algorithm rather than a third party search. Fellows did not comment or vote on Reddit posts, and did not use their new Reddit account to engage in purposes outside of the fellowship. In order to track and return to posts, fellows either used the ‘save’ function on Reddit, or tracked the post through its unique post ID. As a final requirement, fellows did not request access to private pages; during the first week of data collection some subreddits temporarily went private or ‘dark’ in protest to changes in the Reddit API pricing plan. Fellows did not collect data from pages that went dark during this time, only on pages that had open public access. An application programming interface, or API, was not utilized to conduct searches on Reddit.

Informed by Proferes et al. (2021), fellows developed a system to track posts, comments, comment thread order, links and/or media from posts as well as the voting structure on original posts, information of subreddits themselves, as well as the required columns including demographic information of poster. The tracking of posts and comments was developed to closely replicate Reddit’s unique nesting structure and capture the most accurate flow of conversation. Fellows curated their recorded comment threads by excluding comments with text assessed as either repetitive or nonsubstantive. In order to maintain the nesting structure in the event of an excluded comment, the comment thread is continued with the next continuous thread number. All Reddit posts were sorted by time on the Reddit page rather than the default setting of ‘popular’ addressing Proferes et al.’s (2021) discussion of Reddit’s inherent structure and algorithm that tends to drive conversational patterns to the most controversial (Shepherd, 2020; Proferes et al., 2021).

Actual searching and collection of Reddit data was performed in two different ways, fellows either used the main search bar built into Reddit to pull information from a combination of keywords (topic area term and sexuality term) and then narrowed their scope through the date range for data collection (January 2018 - May 2023) and applicability of material; or they went to topic specific subreddits and searched within the subreddit the combination of key terms. Fellows were encouraged to do both to pull a wide range of information, and find applicable posts outside of subreddit communities. For mega threads fellows reviewed content of comments and recorded based on applicability to scope of research and reduced repetitive comments.


TikTok data was gathered through both the mobile app and the desktop websites with researchers choosing which one to use based on personal preference. We decided not to use an application programming interface (API) for data extraction. We instead created new accounts and found videos through searching key terms relevant to each topic group. Some researchers snowballed from the videos found using search terms. Videos were transcribed using the dictate function in Microsoft Word and edited for accuracy by each individual researcher. We excluded videos that were longer than the 3 minute mark. Comments on videos were included if they were relevant to the topic and added to the conversation. We felt that it was especially important to include comments that the creator of the video interacted with or that had a lot of general interaction (i.e. a large amount of likes). Only videos and comments that were posted between January 1, 2018 and May 31, 2023 were recorded. We excluded any videos that were not in english in order to avoid any translation errors. We noted several categories that were unique to TikTok: sound used/relevant lyrics, number of favorites, number of views, number of shares, listed pronouns, and creator interaction.


Fellows on the Twitter team created new, unverified, and unidentifiable accounts. This was done in order to bypass any algorithms that could be found during the search process. Because each Twitter account requires at least one person or organization to follow, I followed ESPN, a sports organization. Data was collected using the advanced search options. Filtering the date range from January 1, 2018 to May 31, 2023, we further enhanced the search by requiring that each post have at least 2 likes. This was done in an effort to reduce data from bot accounts. Through the data collection process Twitter underwent change under CEO Elon musk. Twitter became X and ‘tweets’ became ‘posts’. The functionality of advanced searching did not change.


There were two YouTube collectors for the disability team, Raheleh Ghasseminia and Hana Choi, as the team was additionally divided between physical disability and neurologic disability content with acknowledgement that there would be overlap between the two disability subsections. The group agreed to split into two disability sections since it was anticipated that there would be copious amounts of content around sexuality and disability and that the content would be sufficiently disparate in context to warrant individual attention. The disability and sexuality research is often also differentiated between people living with neurological conditions and those with physical disabilities so our data collection methods reflect this differentiation in sexuality and disability scholarship in general.

Data collectors searched YouTube in an incognito internet browser window with a list of key terms agreed upon a priori by the disability group as a whole. Data collection occurred for each set of key terms until saturation was reached or until comments were no longer relevant. The data collector then moved on to the next set of key terms. Key terms are documented in the data sheets.The research leads decided that YouTube data collectors would not watch the videos but would primarily focus data collection on video comments. This proved both prudent and challenging. The pace of data collection would have been significantly slowed if collectors watched each video in detail. Rather, data collectors watched videos for context if necessary and otherwise focused on the comments and characteristics of the commenters. Taken as a whole, comments were found to be in the vein of gratitude for representation and visibility of the coexistence of people with physical disabilities as sexual beings. Some controversial topics such as sexual surrogacy intersected with these concepts and divided commenters in favor or against such practices. These are initial impressions, however, since data collectors did not code or interpret the data



Anastasiou, D., Kauffman, J. M., & Michail, D. (2014). Disability in multicultural theory. Journal of Disability Policy Studies, 27(1), 3–12.

Andrews, E. E., Forber-Pratt, A. J., Mona, L. R., Lund, E. M., Pilarski, C. R., & Balter, R. (2019). #saytheword: A disability culture commentary on the erasure of “disability”. Rehabilitation Psychology, 64(2), 111–118.

Delkic, M. (2022). Leg Booty? Panoramic? Seggs? How TikTok Is Changing Language.

Jette, A. (2009). Toward a Common Language of Disablement, The Journals of Gerontology: 64(11),1165–1168.

Proferes, N., Jones, N., Gilbert, S., Fiesler, C., & Zimmer, M. (2021). Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics. Social Media + Society, 7(2).

Shepherd R. P. (2020). Gaming Reddit’s algorithm: R/the_donald, amplification, and the rhetoric of sorting. Computers and Composition, 56, 102572.

Wade, D. T. (1996). Epidemiology of disabling neurological disease: How and why does disability occur? Journal of Neurology, Neurosurgery & Psychiatry, 61(3), 242–249.

Available for download on Sunday, September 01, 2024