During a ten-week Human Sexuality fellowship program, one of the focal areas was sex work, undertaken by an interdisciplinary team of graduate students and faculty. Employing an emergent design approach, this team used multiple iterations to refine their data collection process. Initially comprised of sixteen fellows, the Sex Work team sought to compile a comprehensive dataset encompassing various aspects of sex work, as broadly defined, across four social media platforms: Reddit, TikTok, Twitter (aka "X"), and YouTube. The Sex Work Databank eventually amassed nearly 6,000 data entries, with YouTube yielding the largest share.
The data collection process began with the collaboration of general search terms by fellows and faculty leads. Their deliberations and strategies were meticulously documented in a “Data Best Practices Guide.” From June 2nd to June 23rd, 2023, the team held discussions to determine key variables and search terms for use on the chosen social media platforms. Initially, the team was structured into three subgroups: Space, Place, and Identity; Politics and Policy; and Community. Although these subgroups were dissolved early in the data collection process, their initial themes continued influencing data gathering. Notably, the team devised five descriptive tags that would shape their data collection efforts: Brand Building, Policing and Politics, Customer Oriented, Worker Oriented, and Justice and Activism.
Across all platforms, universal parameters for data collection were established, encompassing standard columns, time-bound data, and the application of specific keywords. Thirteen standard data columns were identified, including demographic and identity markers such as race, ethnicity, gender, and nationality. Classifying these markers as "declared" or "perceived" introduced a subjective element into the data collection process. Data was collected from posts made between January 1st, 2018, and May 31st, 2023. One finding was that each platform’s utilization lent itself more towards one or two tags.
Data Use Guide
These guidelines are designed to recognize each social media platform has different forms of online suppression and terms which modify how sex workers discuss and tag their work, e.g. reddit discussions do not suppress the term sex but TikTok requires references to “seggs” or “s3x.” Before data collection began, the team came up with a collaborative list of search terms for their respective platforms and while there is frequent overlap e.g., Accountant and Accountant Life are frequent covert terms regardless of platform, the top five broadest terms were used to collect during the first week. “Broadest” here is defined as the search terms that will capture the largest population of sex workers, conversations on sex work, or sex worker content. At the end of each week-long data collection period a new set of search terms was decided for the following week of data collection. The weekly refinement is designed to recognize the broad nature of sex work and that each sub-industry cannot be captured by manual data collection alone. Weekly refinement drawing from the original search list allows recognition of capturing a variety of sub-industries without capturing every post, video, or comment which may appear under these key terms. Rather, it captures the broader essence of the conversations around sex work posted content which appear on various social media platforms. Additionally, this serves to recognizing the evolving and iterative method of data collection involved on social media where researchers outside the sex work community will encounter new terms and tags by which sex workers distinguish themselves in online spaces. The use of an iterative approach allows each week to include the use of any new terms or tags allowing the researchers to better access the online spaces and language used by the target population.
The below main five tags created 6/30/2023 after the first week of data collection.
Tags were designed to broadly engage in five key thematic areas of content and comments which appeared in relation to the content.
Tags are intended to be used based on the original content, post, or video which created the discourse captured in comments related to the original post. While each original post/video which generates comment discourse is determined w/ a tag, each individual comment attached to the original post/video is not given a new or different tag. The comments under any respective post/video will use the same tag. Additionally, while certain videos/posts might have multiple themes the tags are used to categorize at the broadest level and tags are kept to a minimum of two tags. Tags were determined by the individual are inherently subjective, but their main function serves to guide individuals interested in particular areas of the data and are not set codes which all future researchers must use. For brevity each tag is coded by the acronym for the longer definition name.
BB | BrandBuilding: content or information oriented towards building their brand, marketing, putting out content of their daily lives while being connected to their working information/account.
P | Policing and Politics: content aimed at addressing morality, politics/laws affecting sex workers, clients, or those in the industry, trafficking, criminalization, or policing.
CO | Customer Oriented: Coming from customers or information intended for customers w/ information and resources.
WO | Worker Oriented: coming from workers or information aimed at resource sharing, asking questions, safe practices, business practices for sex workers.
JA | Justice and Activism: aimed at harm reduction, destigmatization, normalization of sex work and safety practices, activism in Sex Work or outside of Sex Work.
Saturation was subjective to individuals working on each social med platform within the team, however the below definitions were used as the guidelines:
No new, additional material or commentary that adds information differing significantly from other comments.
When a search term criteria pulls the same type of original content (OC) which has minimal variation from other content and the comments are consistent and similar across these types of OC. A common example of this is trending video formats on TikTok such as “get ready with me.”
If a comment or threads devolved significantly into personal attacks or “trolling” unrelated to the original comment, or thread the comments would be collected to reach saturation of the theme of the conversation, remaining undocumented comments were counted but not included.
This dataset was collected on Sex Work (broadly defined) on four different social media platforms: Reddit, TikTok, Twitter (aka X), and YouTube. Nearly 4000 lines of data were collected in the Sex Work Databank, with the most data being collected from YouTube. General search terms were created by a team of interdisciplinary fellows and faculty leads in the early stages of conceptualizing data collection. Over the course of ten-weeks the Sex Work team followed an emergent design of building a digital sex work dataset and engaged in several rounds of the iterative process to create a robust data file. Data collection conceptualization was documented in a Data Best Practices Guide.
From June 2nd to June 23rd, 2023 topic groups met to discuss key variables and search terms to be used on the various social media platforms; data collection began on June 24th, 2023. Initially, the team was comprised of three subgroups: Space, Place, and Identity; Politics and Policy, and Community. While these subgroups were dissolved early on in the data collection process, the initial themes did impact data collection. One instance of these themes is found in the five tags created by the team of sex work fellows.
Universal parameters (across all platforms) for data collection included: standard columns, time-bound data, and the use of keywords. There were thirteen (13) standard data columns which included demographic/identity markers of race, ethnicity, gender, and nationality. These markers were not always proclaimed and therefore became subjective to the fellow collecting the data. Demographic were characterized as either “declared” or “perceived”. Fellows collected data posted between January 1st, 2018 to May 31st, 2023. Keywords that indicated sex work were discussed and could be used across platforms. All keywords can be found in on page 13.
This table is for inputting the search terms used each week for data combing on the respective platform. Input the appropriate terms used within your team for the areas you systematically pulled from.
Sex for Trade
6/30 - 7/7
Sex for Trade
Collected from Sex Worker Community Forums. See Reddit Process Notes
No changes from previous week
Black spicy accountant
black s3x work
Black seggs work
poc s3x work
Poc seggs work
Bipoc s3x work
Bipoc seggs work
Any limiters or potential limiters:
*please highlight for suggestions as they come up during your data collection
*limiting search terms for sites to larger platforms such as OnlyFans, Seeking, ManyVids, Chaturbate
SITE SPECIFIC TERMS
Sex for Trade
*****below is list of sex worker specific threads some open to nonSW some are not*** they can all be searched and collected from
r/Fightcampiracy -- dismantling illegal websites that pirate content from OnlyFans, ManyVids, etc.
Sex Worker Stories
Sex Worker Relationships
Sex Worker Clients
Sex Work News
r/CyberSugar - SWs & SBs coexist with no judgements omg wow o.o
r/sugarlifestyleforum - Proceed with caution.
r/SugarDatingForum - Proceed with more caution.
DL or Down Low
ASP (Adult Service Provider)
Escort or escort service
On the Stroll
Day in the life/dml of blank
s3x (and all varieties of s3x work)
asp (adult service provider)
fsp (full service provider)
corn star, (corn emoji) star
MSW (male sex worker)
emojis (eggplant, peach, water, etc.)
Sub-group specific terms (we may or may not incorporate these and/or use them as reference points as we move through data collection)
policy, politics, Sesta/Fosta, act CSEC (commercial sexual exploitation of children), trafficking, pimp, prostitution, sex trafficking, decriminalization, legalization, policy, government response, stigma, criminalization, public opinion, sex workers rights, political discourse, abuse, exclusion, harassment, police, policing, law enforcement, privacy
movement, collective, organization, pimp, trafficking , how to, starting, beginning, harm reduction, bad date lists, blacklisted, baby (+stripper/escort/etc), safety, scams, resources, STI testing, SWOP (Sex Worker Outreach Project), college (workshops, speakers, lectures, panels, etc.), conferences, tips, marketing/branding, harassment, risk, risk exposure, client screening, privacy
Space, Place, Identity
How they’re defining and presenting themselves (e.g., sex worker vs. college student)
Mention of dating apps, hookup sites, or distinctly sex work sites (Grindr, Tinder, Seeking Arrangements, etc.)
Mention of sites where content is sold (OnlyFans, ManyVids, Chaturbate, Fansly)
Stigma or shame
Bars, nightclubs, raves, etc.
Residence (bedroom, living room, etc)
Social status (followers, fans, etc.)
Web platforms / internet / technology
Online vs. offline
Indoors vs. outdoors / physical vs. digital / hybrid
Blogs or websites
Clients / clientele
Femininity / masculinity
Authentic self / authenticity
FTM, MTF, trans, etc.
Platform Specific Information
The goal of Twitter data collection was to collect and examine the kind of conversations generated around sex work and sex workers on Twitter. The data collection aimed at providing answers to questions such as how were sex workers using Twitter? What demography of people were the tweets on sew work coming from? Are sex workers using Twitter for brand building or for justice and advocacy? And what level of policing goes on on Twitter around the subject of sex work and sex workers?
Twitter data collection began with fellows creating new, unidentified accounts. This approach was specifically used to ensure a clean slate for searching and an ability to bypass any age-restricted content for sex work/er related posts. Creating a new Twitter account requires following at least one other account, with a list of suggested accounts to follow appearing during the creation process. Fellows selected one account from the suggested list, often a celebrity or other prominent public figure unrelated to the data collection.
Data was collected using the ‘advanced search’ component within Twitter’s website. This feature allowed fellows to input a search term and filter results to specific date ranges. Fellows limited results to specific months within the timeline of interest (January 2018 – May 2023). Results were also filtered to only include Tweets with 2 or more Likes in an attempt to minimize results from spam or bots. The primary search terms used on Twitter included: sex work, sex workers, swers, escort, and hooker. Certain demographic terms were added with the primary search terms to obtain a representative sample. The demographic terms used included: Black, male, gay, and trans.
Even with advanced search filtering, thousands of tweets were available for each month. Due to time constraints, fellows used discretion to filter through results and collect the most relevant data.
When searching YouTube, data was collected using incognito mode and clearing data between searches with every data search to keep searches as impervious to a pre established algorithm as possible. To that same effect when searching YouTube, it was required that we were not signed into personal or professional accounts. Search terms used were determined by topic groups - specific to disability, sex work, family, and housing. Because of the algorithm’s penchant for showcasing the videos of white, cisgender women sex workers, other search terms were added–such as “male escort”--to gather more varied data.
Information on the video, such as viewership and interaction statistics, were collected as well as comments that were deemed relevant to the topic of interest, excluding off topic or spam-like comments from being collected. Fellows were instructed to collect from any one comment section until they had met consistent saturation of that comment section - this was at the discretion of the data collectors.
The data collected focuses on comments rather than the YouTube videos themselves. YouTube Shorts were barred from data collection as well as videos under “YouTube also suggests or others also watched…” algorithmic redirection. Videos were only allowed to be collected off of a fresh YouTube search under the previous conditions.
Why were comments collected? Comments showcased current conversations around sex work, ranging from positive, supportive comments (which were generally left untagged) to biblical verses and shaming. Considering the tagging system, YouTube had the most untagged comments. This was partially because of the supportive comments, but also because of how conversations would devolve from being about sex work to name calling and trolling.
How we collected TikTok Data
TikTok data was gathered through both the mobile app and the desktop websites with researchers choosing which one to use based on personal preference. It was determined by the TikTok team that we would not use an application programming interface (API) for data extraction partially due to how sex work is already censored and targeted on the app. The sex work TikTok team instead created one new account that was shared among the researchers 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. Videos that featured any mention of sex work, negative or positive, that resulted from our search terms were included to thoroughly investigate how sex work is presented on TikTok. We excluded videos that were longer than the 3 minute mark. Comments on videos were included if researchers felt 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.
Tiktok can be accessed through both the phone mobile app and on desktop computers through tiktok.com, but there are some differences between the mobile app and website. Notable differences include pronouns for accounts only appearing on the mobile app, some emojis used in account profile descriptions only shown in the mobile app, and a display stating whether a comment was liked by the creator only appearing in the mobile app. The mobile app also allows the user to see exactly who replies were directed to whereas on the website all replies appear under the original comment as one thread. For this reason, comments for sub threaded replies were not recorded and that column was left blank since there was no way to gather this data from the website and researchers would have to manually count responses to replies in the mobile app.
There were several categories that were added to the data sheets for TikTok including: sound used/relevant lyrics, number of favorites, number of views, number of shares, listed pronouns, and creator interaction. TikTok has the unique feature of “sounds” which are sound clips that can include music and lyrics, conversations, or sound effects which creators use in their videos. Sounds often tread on TikTok or are used in certain ways, so we felt it was important to record what sound may have been used or any relevant lyrics or words in them. TikTok also has some features it records that other platforms may not such as favorites, views, and shares. Favorites are a way users can save a video, views record how many views the video received, and shares record how many times users send that video to someone else. Favorites, views, and shares are only recorded on videos and not comments. The TikTok app allows creators to put their pronouns on their account page, so it was recorded when a creator would do so. Finally, TikTok displays whether a comment has been liked or replied to by the creator of a video, so any creator interaction with comments was recorded. Creator interactions were only recorded for comments. TikTok captions are also unique as the captions to videos include any hashtags that were used. Because of this, we decided that hashtags at the end of the captions would go in the Hashtags Used column while the actual content of the caption would be in the Caption column. Hashtags were only included in the caption when they were part of a sentence. (Example: “Supporting #sexworkers!”)
How to use the Tiktok Data
Below are the columns that were used:
Original, Threaded, or Subthreaded- original posts are bolded while threaded and subthreaded posts are not bolded
Original refers to any post discovered while using search term or snowballing
Threaded- is reserved for comments in direct response to a video
Subthreaded- For comments that are in response to other comments
Date Posted - the date the Tik Tok video or comment was posted. Our time frame for data was January 1, 2018 - May 31, 2023, no videos or comments created before or after then were recorded.
Text of post- researchers inserted any text the video displayed onscreen or the text in a comment
Audio Transcription- researchers used Microsoft Word Diction/Transcript buttons for video content transcripts and then edited the transcription as needed
Sound Used/Relevant Lyrics- researchers recorded what TikTok sound was used and if any relevant lyrics or words was included in it
Person/organization- researchers recorded if the creators account was either an individual or organization
Medium- Researchers used Tiktok
Followers- researchers recorded how many followers the creator had
Following- researchers recorded how many people the creator was following
Views- researchers recorded how many views the video had; this column did not apply to comments
Likes- researchers recorded how many likes the video or comment got
Comments (on/off/limited)- researchers recorded the number of comments the video or comment received or if comments were disabled; this column did not apply to replies to comments
Shares- researchers recorded how many times the video was shared; this column did not apply to comments
Favorites- researchers recorded how many times the video had been favorited; this column did not apply to comments
Verified- researchers recorded if the creators account was verified or not
Pronouns- researchers recorded any pronouns that were listed on the account’s page
Gender- researcher recorded the creator’s gender
Declared or perceived- researchers recorded if gender was declared by creator or perceived by researcher
Race/Ethnicity- researchers recorded the creator’s race/ethnicity
Declared or perceived- researchers recorded if race/ethnicity was declared by creator or perceived by researcher
Nationality - researchers recorded where the creator was from
Declared or perceived- researchers recorded if nationality was declared by creator or perceived by researcher
Other Relevant Identities - researchers recorded any other identities the creator had that might be relevant such as sexuality, occupation, etc.
Location if known- researchers recorded the location of the creator
Hashtags Used- researchers recorded what hashtags the video used
Caption text- researchers recorded the caption of the video, leaving out the hashtags and instead putting them in the hashtag unless they were used as part of a sentence
Sub-Setting/modality- researchers recorded what the sub-setting/modality was. These categories were original video, video response, duet, stitch, comment, and reply.
Creator interaction- researchers recorded creator interaction for threaded comments. These categories included creator replied, video response, creator liked, and comment by creator.
Image description- researchers described relevant aspects of the video being played
Collected By- the researcher that collected the data recorded their name
Tag- this is where tags determined by the sex work team were inputed
Other Notes- comments by researcher
Organization and Strategization in Data Collection
There were several different search strategies used across the sub-teams within the TikTok Team. The sex work TikTok team separated search terms among themselves, using: s3x work, seggs work, spicy accountant, accountant, s*x work, $ex work, swork, and sworker. Due to the high likelihood of repeated content within these terms, the sex work TikTok team shared one account. After we felt we reached saturation from our original terms, other modifiers were added to target minority groups and perspectives. Some of these modifiers included queer, black, trans, and poc and were put in front of the original search terms.
Base Search Terms Used: s*x work, $ex work, swork, sworker, s3x work, seggs work, spicy accountant, accountant, 304tok, 304
Search Terms with Descriptors (not all are listed): black s3x work, black seggs work, poc s3x work, poc seggs work, queer seggs work, queer s3x work, male seggs work, male s3x work, male s3x worker, queer accountant, poc accountant, black spicy accountant, latinx spicy accountant, trans spicy accountant
Reddit data collection began with one fellow creating a new, unidentified account that was shared with other team fellows and the data manager. This approach was specifically used to ensure a clean slate for searching and reduce algorithmic bias from previous account activity. From there, to track which posts had been documented, posts were saved to the main account but no other user activity was done with this shared account.
Recognizing the difference between searching broadly in the reddit search engine for terms related to sex work which primarily generates civilian centered discourse on the merits of sex work particularly on aspects of value of the labor and stigma, the reddit team switched to data collection from sex work specific community forums. The switch to specific forums began on 7/7/2023 with the intent to switch to searching different terms at a later date and move away from community specific sex work forums.
The rationale for switching is two-fold, first it centers sex worker discourse in community built for sex workers which is inherently different from civilian centered discourse and provides a wider array of topics. Secondly, it allows a diverse range of discourse to be collected across different types of sex work which is a broad industry. The original list of community forums included 48 unique groups and after parsing through this list 20 were still available for data collection. The primary reason a thread was not available for data collection was the thread had been banned by reddit because there was no moderator. Other reasons included bans from reddit for violating terms of service, private communities, and no posts.
Again, topics were collected by date and each researcher started in a community forum collecting threads to saturation and by date before moving to the next sex work community thread. Unfortunately, due to unforeseen circumstances, data collection stopped after fellows were no longer able to participate in data collection as of 7/21/2023.
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