As part of the Sexuality Summer Research Fellowship organized and facilitated by the California Institute for Integral Studies, this dataset focuses on 2SLGBTQI+ experiences and perceptions of housing insecurity and homelessness. Housing and rental affordability pressures, particularly for low-income earners, have become a pressing issue worldwide, especially as inflation has escalated over the past three years (Barrett, 2022). Much of this can be attributed to the build-up and fallout of the global financial crisis of 2008, where the culmination of stagnant growth in real wages and a sustained reduction in access to social security and welfare provisions has pushed many households into poverty and caused rising homelessness (Delclós & Vidal 2021; Broadbent, et al., 2023). The COVID-19 pandemic exacerbated much of these pre-existing pressures, as measures to curb the spread of the virus such as lockdowns, revealed the absence of safe and secure housing, especially for vulnerable groups such as the 2SLGBTQI+ community (Salerno, et al., 2020; Lederer et al., 2021).
According to Cox et al. (2017), housing insecurity exists on a continuum of multidimensional housing-related issues, including stability, affordability, quality, safety, neighborhood safety, and neighborhood quality. Homelessness also exists on the continuum as the most extreme form of housing insecurity, which consists of the lack of a fixed, regular, and nighttime residence (Broton & Goldrick-Rab, 2018; Cox et al., 2017). Queer-and-trans youth are disproportionately overrepresented in such experiences as 28% of 2SLGBTQI+ youth reported experiencing homelessness or housing instability at some point in their lives in the United States while 24% of youth experiencing homelessness in the UK identify as 2SLGBTQI+ (Bhandal & Horwood, 2021; DeChants, et al. 2021). Family rejection when “coming out” or being “outed” is a leading factor in causing 2SLGBTQI+ homelessness, leading to many queer-and-trans folx to rely on informal support networks for temporary shelter such as couch surfing (Abramovich, 2012; Petry, et al. 2021). Those who do end up on the streets can be exposed to dangerous and harmful situations such as substance misuse and violence. Furthermore, living on the streets can have a longer-term impact on the emotional, physical, and psychological well-being of queer-and-trans folx, especially for youth (Abramovich, 2012). As shelter services predominantly operate on a cisheteronormative basis, it means that systemic and cultural barriers prevent queer-and-trans folx from accessing safe and supportive services that cater to their specific needs (Abramovich, 2017; Fraser, et al. 2019).
Drawing from the range of literature that was reviewed as part of the fellowship programme, keywords were selected that are often associated with housing insecurity and homelessness as well as queer and gender-diverse identities. These keywords would form the basis of what content would be collected from the social media platforms (Twitter/X, TikTok, Reddit and YouTube), therefore, they were organised into clusters to help generate the most relevant search results. These clusters included “priority identity”, “housing situation”, “sub-identity”, “sexual acts”, “housing event”, and “reactions”.
Although the use of keyword search functions differs per social media platform, the keyword clusters helped systematise the approach to the data collection as two/three keywords were selected from the clusters. For example, priority identity (queer), housing situation (homeless), and housing event (kicked out) was one combination that was paired together to search on social media. The targeted timeframe to collect data was from January 1, 2018, to May 21 2023. However, the labour intensity levels to collect data differed greatly per social media platform; thus, resulting in varying time periods for some platforms. Information on the methodological approach per social media platform is listed below.
Priority Identity: The search terms in this cluster aimed to reflect the diverse sexual, relational, and gender identities of posters on social media. These keywords included both the holistic terminology (e.g., “LGBTQ,” “sexual identity,” “CNM,” “sexuality), specific identity categories (e.g., “transgender,” “gay,” “non-binary,” “polyamorous”), as well as alternative terms (e.g., “enby,” “trans”). All identities in this cluster were considered priorities for data collection.
Housing Situation: Based on the work of Cox and colleagues (2017), the housing situation cluster utilized a variety of terms to capture the continuum of housing experiences. Some of the keywords in this cluster specified the housing situation (e.g., “homelessness,” “renting,” “couch surfing,” “sleeping in car”) while others were more descriptive of the types of housing situations (e.g., “encampment,” “co-op,” “halfway house,” “tiny house”). Additionally, this cluster included keywords related to socio-regional categories (e.g., “rural,” “urban”), as well as U.S. cities that are stereotyped as being places of high rates of homelessness (e.g., “Seattle,” “San Francisco,” “Portland”). For the purposes of collecting data, nine of the keywords were prioritized (e.g., “homelessness,” “sleeping in car,” couch surfing,” “homeless,” “encampment,” “tent city,” “homeless shelter,” “unhoused”), while the term “landlord” was included as a secondary priority.
Sub-Identity: The search terms in this cluster reflected the intersectional values of the housing team and enabled the collection and classification of specific instances of housing insecurity with respect to the diverse experiences of social media posters. The cluster included keywords, which represented the religious, ethnic, racial, educational, and abilities of posters. All items in this cluster were considered of secondary priority in data collection.
Sexual Acts: The cluster of sexual acts included keywords that aimed to capture the sexual experiences of social media posters. Some of the keywords had specific institutional implications in health and law (e.g., “sexual health,” “indecent exposure,” “assault,” “sex work”), which may capture the unique experiences of unhoused people with regard to their health and well-being. Other terms specified sexual behaviours (e.g., “masturbation,” “oral sex,” “threesome”), internet slang terms (e.g., “seggs,” “s3x”), or more general themes related to sexuality (e.g., “pleasure,” “sex”). The keywords “sexual health” and “sex” were considered secondary priorities in the data collection.
Housing Event: For this cluster of keywords, the team sought to explore the different social dynamics that might lead to housing insecurity and enable the collection of the lived experiences of posters. Some of the keywords specified economic relations related to housing insecurity (e.g., “priced out,” “rent raised,” “evicted,” “housing crisis,” “renting,” and “gentrification”), while others were related to intimate relationships involving physical violence (e.g., “violence,” “domestic violence,”), family dynamics (e.g., “family rejection,” “kicked out,” “coming out,”), and limitations to accessing housing (e.g., “immigration,” “transphobia,” “homophobia,” “criminal record”). For data collection, the term “evicted” was of secondary priority.
Reactions: Lastly, the housing team sought to capture the public reaction to housing insecurity and homelessness in the data set through the use of the reactions cluster. The keywords in this cluster reflected social movements (e.g., “NIMBY,” “YIMBY,” “mutualaid,” “loveislove”) and state power (e.g., “police,” “tent sweep,” “camp sweep,” “mass incarceration”) related to housing. The entire cluster was considered of secondary priority during data collection.
Platform Specific Information
The data collected focused on comments rather than the content of the original YouTube video. Fellows searched YouTube using incognito mode making sure they were not signed into personal or professional accounts. Videos were only allowed to be collected from a fresh YouTube search under the previous conditions. Fellows cleared data between searches with every data search to keep searches impervious to pre-established algorithms. In addition to comments related to gender, sexuality, and housing insecurities, the following variables were collected specifically across the YouTube platform team: Title of video, the caption of the video, video date posted, video creator, video subscribers, video verification status, and the number of views, likes, and total video comments. Excluding spam-like comments, anyone’s comments were available for collection, and the researcher’s goal was to meet consistent saturation of that comment section, at their discretion.
On YouTube, users have the option to reply to multiple ongoing conversations. The following nesting level guidelines were used to maintain conversational integrity when transferring information to the dataset and allow for better context when reviewing the data. The data is sourced from 2011 to 2023.
X.Y.Z Comment Organization:
X = main comment number
Y = threaded comment number
Z = sub-threaded comment number can extend for # of thread nests
The data collected from Reddit focused on both the original posts as well as the comment threads. To collect the data, each post and comment was individually copied and pasted into the dataset and coded in a structured process to indicate nested threads. Fellows created new Reddit accounts to collect the data. The data was sourced primarily from 2018 and 2019 and included the key terms “homeless,” “LGBT,” and “gay.”
Over the course of the summer, the social media platform previously known as “Twitter” experienced a name change and is now called “X” (Mac & Hsu,2023). Several additional changes were made prior to and during the platform’s rebranding. “Tweets,” the 280-character short-form posts that comprise the social media platform, are now called “posts.” Similarly, “retweets” have been renamed “reposts,” and “quote tweets” are simply referred to as “quotes.”
Twitter/X data collection began with creating new Twitter/X accounts meant solely for data collection with the Fellowship. These accounts were made to procure as neutral search results as possible to avoid personal accounts turning up results that appeared to be impacted by personal social media tendencies (i.e. algorithm). The data consisted of tweets/posts, retweets/reports, and replies, and the only data captured was originally published from January 1 2018, to May 21 2023. We employed the advanced search mechanism to apply filters and refine our searches in any of the five search categories: words (including language), accounts, filters, engagement, and dates.
We agreed to use the available “words” search settings (“all of these words,” “this exact phrase,” “any of these words,” “none of these words,” and “these hashtags”) to input each topic group’s unique search terms. The language search setting was set to “English” or “any language” with the understanding we would only capture tweets/posts written in English. We did not adjust or enter any information for the “accounts” or “filters” search categories, but we did agree to set the minimum likes option, found in the “engagement” category, to two. This helped filter out tweets/posts from suspected bots while still showing tweets/posts from users who might have lower engagement. After populating these criteria and pressing “search,” we agreed to use the “latest” search results tab instead of the algorithm-curated “top” search results tab.
In addition to these guidelines, the Twitter/X-Housing researchers also enacted a few standard procedures. Two researchers were assigned to Twitter/X. We divided collection responsibilities by assigning one researcher to tweets/posts published in 2018-2019 and the other to the 2020-2023 timeframe. To include both hashtags and non-hashtagged words in search results, we searched only for words using the “all of these words” search option. The determination of which keywords to use was collaborative across the Housing team and within the Twitter/X-Housing researchers. Keyword combinations (e.g., queer + encampment) showed contrasting results between the 2018-2019 and 2020-2023 timeframes, both in the quantity and content of the tweets/posts.
Data was gathered through both the mobile app and the desktop websites with researchers choosing which one to use based on personal preference. New accounts were created and videos were found by searching keywords from the clusters. This sometimes allowed for snowballing from the videos found using search terms. Videos were transcribed using the dictate function in Microsoft Word and edited for accuracy by individual researchers. 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 number 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 favourites, number of views, number of shares, listed pronouns, and creator interaction.
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Dejarlais-deKlerk, Kristen; Brooks, Thomas; Johnson, MoAndra; Koski, Siiri; Rainey, Emma; and Swayne, Vivian, "Fluid Homes: Navigating Gender, Sexuality, and Housing Insecurity Discourses on Social Media" (2023). Summer Sexuality Fellowship. 2.
Available for download on Sunday, September 01, 2024