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For additional resources on how to fairly and accurately report on transgender people, please see " In Focus: Covering the Transgender Community " and visit glaad. Transgender women are not cross-dressers or drag queens. Drag queens are men, typically gay men, who dress like women for the purpose of entertainment.


Try out PMC Labs and tell us what you think. Learn More. Understanding gender transition sentiment patterns can positively impact transgender people by enabling them to anticipate, and put support in place for, particularly difficult time periods.

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Yet, tracking sentiment over time throughout gender transition is challenging using traditional research methods. Computational sentiment analysis and statistics were used to analyze 41 posts from Tumblr transition blogs online spaces where transgender people document gender transitions to understand sentiment patterns over time and quantify relationships between transgender identity disclosures, sentiment, and social support.

Findings suggest that sentiment increases over time on average throughout gender transition, particularly when people receive supportive responses to transgender identity disclosures. However, after disclosures to family members, people experienced temporary increased negative sentiment, followed by increased positive sentiment in the long term.

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After transgender identity disclosures on Facebook, an important means of mass disclosure, those with supportive networks experienced increased positive sentiment. With foreknowledge of sentiment patterns likely to occur during gender transition, transgender people and their mental healthcare professionals can prepare with proper support in place throughout the gender transition process.

Transgender people those whose gender differs from the gender they were ased at birth, 1 including nonbinary people face substantial mental health disparities as compared with the general population. At the same time, as one transitions, they may face discrimination and harassment, which have negative mental health effects. Sentiment over time during gender transition was examined using computational sentiment analysis of 41 posts from transition blogs on the social media site Tumblr.

These are blogs where transgender people document personal s of their experiences. With this new understanding of sentiment patterns during gender transition, steps can be taken to decrease mental health disparities faced by a substantially marginalized population. A small body of research has examined transgender issues in medical and health informatics. The present study expands on this work by using social media data to illuminate gender transition sentiment patterns, which can be of great use to medical informatics researchers and transgender healthcare providers.

Self-disclosure has been found to lead to improved mental health, physical health, and self-esteem, 27 and is necessary to receive social support. Social support is widely found to have a moderating effect on the relationship between transition status and mental health.

Computational text analysis methods such as sentiment analysis can be powerful tools for researchers to extract meaning and themes from large bodies of text. Researchers have used computational linguistic techniques like sentiment analysis, sometimes paired with social media data, to understand social phenomena such as depression, 41—44 mental health more broadly, 45—47 and even transgender topics. Transition blogs are a genre of Tumblr blog in which people document their gender transition. Commonly, these blogs include diary-like entries discussing social, medical, and legal aspects of transition: discussion of the coming out process and resulting support or rejection, physical and mental changes, medical procedures, and name and document changes.

Methods are summarized in Figure 1.

The tumblr transgender clothing exchange

Parts of this work draw from a larger study. Data collection did not include photos, images, or visual content of any kind. Computational sentiment analysis was used to measure sentiment in each blog post.

While computational sentiment measures have been found to be somewhat of a proxy for emotional well-being, 3961 these measures have accuracy limitations 3961 and are not a clinical measure of mental health. A machine learning classifier was built to detect a particular type of disclosure: Tumblr posts describing transgender identity disclosures in other contexts see Table 1 for example posts. A post counted as a transgender identity disclosure if it described a disclosure that seemed to have occurred within 2 weeks prior to the post describing it.

The first step involved building a training set of positive and negative examples of transgender identity disclosure posts in the dataset. An iterative approach was used to build a sufficient training set, which included several rounds of manual coding and machine learning.


To establish interrater reliability, 2 coders OLH and NA first coded 50 posts as either recent transgender identity disclosures or not, and reached acceptable interrater agreement at a kappa of 0. OLH then coded the remaining training data. The Python SciKitLearn library 62 was used to build the machine learning classifier. AdaBoost was most accurate, with an accuracy of 0. Next the classifier was applied to the full dataset.

The model classified posts as positive, which OLH then manually coded to ensure that the computational coding did not include false positives. Manual coding identified a total of posts describing recent transgender identity disclosures. The high of false positives indicates that the model had poor specificity, a limitation that was addressed by manually coding all positively classified posts. Unfortunately, it is not possible to identify false negatives. For each transgender identity disclosure post, the disclosure audience s was manually identified by reading the post.

) i’d love to meet and become friends with a trans but unfortunately i live in a small town and as far as i know there’s no trans anywhere near here. i wish i was in a bigger area.

This resulted in a set of 20 disclosure audience types Table 1. Each post that described a transgender identity disclosure was manually coded for whether the poster described their audience as being supportive in response to the disclosure yes, no, partially, or unknown. This was later simplified to a binary variable supportive response or not after observing few posts in the partially and unknown. As a result of the 3 steps, each post in the dataset had the following information:.

Regression models were built to understand the relationships between these variables. Using posts as the unit of analysis, all models include average sentiment in the time period after the post days, days, or days as the dependent variable.

Independent variables included whether or not the post described a transgender identity disclosure, and whether or not the disclosure received a positive response. The models also included all available control variables, including blogger demographics and characteristics of posts. Because the data did not meet the assumptions required for linear regression, robust linear regression was used instead.

However, because it is more informative to separate out different types of disclosures, this is done according to the primary disclosures that related to the liminality stages: 515364 family section R. are summarized in Table 2. Table 3 provides clarity about the organization of models in Table 4.

are detailed in Table 4 and sections R. Robust linear regression models showing average negative and positive sentiment in time period following posts. Values are coefficient SE. Control variables included in each model; details omitted for space : involuntary disclosure binary indicatorgender, age, of likes, of replies, of reblogs, word count, year, average negative emotion in time period before post, average positive emotion in time period before post.

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Please see Supplementary Material for full regression tables. A total of 41 Tumblr posts by bloggers were analyzed. Posts had an average word count of Demographic data were found in blog descriptions. Posts were from to collected in early Januarywith Bloggers described on average 1.

Support moderates the relationship between disclosure and negative sentiment in the short term. Model 1 Table 4 shows that posts describing transgender identity disclosures with supportive responses were followed by fewer negative emotion words in the month following. Disclosure is associated with increased positive sentiment in the long term, whether or not the audience was supportive.

Models 5 and 6 Table 4 indicate that posts describing transgender identity disclosures were followed by more positive emotion words in the 3 and 6 months following, but support was not a moderating variable. That is, people saw positive sentiment increases in the 3 and 6 months post disclosure whether or not their disclosure audience responded supportively.

Outcome measures are the percentage of total words in a post that were part of the LIWC positive emotion dictionary. This level of detail will not be provided for each result, but is provided here to help readers interpret Table 4. Here, the independent variable of interest is a binary indicator of whether a post described a recent disclosure to a family member.

Family disclosures are associated with increased negative sentiment in the short term. Posts describing transgender identity disclosures to family members were followed by more negative emotion words in the month following, according to Model 7 Table 4. Importantly, support is not a ificant moderating variable here; even those who received supportive responses from family members experienced increased negative short term sentiment.

Family disclosures are associated with increased positive sentiment in the long term. Model 12 Table 4 shows that posts describing transgender identity disclosures to family members were followed by more positive emotion words in the 6 months following.

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Again, support was not a moderating variable. Positive sentiment increased whether or not people received positive responses after disclosing to family members. The independent variable of interest is a binary indicator of whether a Tumblr post described a Facebook disclosure. Support moderates the relationship between Facebook disclosures and positive sentiment in the short term and long term. Models Table 4 indicate that posts describing transgender identity disclosures on Facebook with supportive responses were followed by more positive emotion words in both the short term and the long term.

None of the models show a direct ificant relationship between Facebook disclosures and sentiment.

This work contributes an understanding of the patterns in sentiment changes throughout gender transition. Next, during the transition stage, which involves transgender identity disclosures on Facebook, positive sentiment increases. The initial decreased sentiment shown in Figure 3 is likely a result of the combined impact of family disclosures along with minority stressors like discrimination, harassment, and disapproval from others, and personal discomfort in the early stages of transition. Conceptual visualization of gender transition sentiment patterns over time on average.

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