Investigating Gender Euphoria and Dysphoria on TikTok: Characterization
and Comparison
- URL: http://arxiv.org/abs/2305.19552v1
- Date: Wed, 31 May 2023 04:38:38 GMT
- Title: Investigating Gender Euphoria and Dysphoria on TikTok: Characterization
and Comparison
- Authors: SJ Dillon, Yueqing Liang, H. Russell Bernard, Kai Shu
- Abstract summary: This paper extends current research on gender euphoria and gender dysphoria to analyze online communities on TikTok.
Our findings indicate that gender euphoria and gender dysphoria are differently described in online TikTok spaces.
Our results show that gender euphoria is described in more similar terms between transfeminine and transmasculine experiences than gender dysphoria.
- Score: 11.3960616604104
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the emergence of short video-sharing platforms, engagement with social
media sites devoted to opinion and knowledge dissemination has rapidly
increased. Among the short video platforms, TikTok is one of the most popular
globally and has become the platform of choice for transgender and nonbinary
individuals, who have formed a large community to mobilize personal experience
and exchange information. The knowledge produced in online spaces can influence
the ways in which people understand and experience their own gender and
transitions, as they hear about others and weigh that experiential and medical
knowledge against their own. This paper extends current research and past
interview methods on gender euphoria and gender dysphoria to analyze what and
how online communities on TikTok discuss these two types of gender experiences.
Our findings indicate that gender euphoria and gender dysphoria are differently
described in online TikTok spaces. These findings indicate that there are wide
similarities in the words used to describe gender dysphoria as well as gender
euphoria in both the comments of videos and content creators' hashtags.
Finally, our results show that gender euphoria is described in more similar
terms between transfeminine and transmasculine experiences than gender
dysphoria, which appears to be more differentiated by gendering experience and
transition goals. We hope this paper can provide insights for future research
on understanding transgender and nonbinary individuals in online communities.
Related papers
- Gender Trouble in Language Models: An Empirical Audit Guided by Gender Performativity Theory [0.19116784879310028]
Language models encode and perpetuate harmful gendered stereotypes.<n>Gendered terms that do not neatly fall into one of these binary categories are erased and pathologized.<n>Our findings lead us to call for a re-evaluation of how gendered harms in language models are defined and addressed.
arXiv Detail & Related papers (2025-05-20T08:36:47Z) - Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
Existing machine translation gender bias evaluations are primarily focused on male and female genders.
This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words)
We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
arXiv Detail & Related papers (2024-07-23T08:13:51Z) - Building Bridges: A Dataset for Evaluating Gender-Fair Machine Translation into German [17.924716793621627]
We study gender-fair language in English-to-German machine translation (MT)
We conduct the first benchmark study involving two commercial systems and six neural MT models.
Our findings show that most systems produce mainly masculine forms and rarely gender-neutral variants.
arXiv Detail & Related papers (2024-06-10T09:39:19Z) - The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender
Characterisation in 55 Languages [51.2321117760104]
This paper describes the Gender-GAP Pipeline, an automatic pipeline to characterize gender representation in large-scale datasets for 55 languages.
The pipeline uses a multilingual lexicon of gendered person-nouns to quantify the gender representation in text.
We showcase it to report gender representation in WMT training data and development data for the News task, confirming that current data is skewed towards masculine representation.
arXiv Detail & Related papers (2023-08-31T17:20:50Z) - VisoGender: A dataset for benchmarking gender bias in image-text pronoun
resolution [80.57383975987676]
VisoGender is a novel dataset for benchmarking gender bias in vision-language models.
We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas.
We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
arXiv Detail & Related papers (2023-06-21T17:59:51Z) - "I'm fully who I am": Towards Centering Transgender and Non-Binary
Voices to Measure Biases in Open Language Generation [69.25368160338043]
Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life.
We assess how the social reality surrounding experienced marginalization of TGNB persons contributes to and persists within Open Language Generation.
We introduce TANGO, a dataset of template-based real-world text curated from a TGNB-oriented community.
arXiv Detail & Related papers (2023-05-17T04:21:45Z) - Much Ado About Gender: Current Practices and Future Recommendations for
Appropriate Gender-Aware Information Access [3.3903891679981593]
Information access research (and development) sometimes makes use of gender.
This work makes a variety of assumptions about gender that are not aligned with current understandings of what gender is.
Most papers we review rely on a binary notion of gender, even if they acknowledge that gender cannot be split into two categories.
arXiv Detail & Related papers (2023-01-12T01:21:02Z) - Theories of "Gender" in NLP Bias Research [0.0]
We survey nearly 200 articles concerning gender bias in NLP.
We find that the majority of the articles do not make their theorization of gender explicit.
Many conflate sex characteristics, social gender, and linguistic gender in ways that disregard the existence and experience of trans, nonbinary, and intersex people.
arXiv Detail & Related papers (2022-05-05T09:20:53Z) - Exploring a Makeup Support System for Transgender Passing based on
Automatic Gender Recognition [12.92294657841358]
We explore how machine learning could potentially be appropriated to support transgender practices and needs in non-Western contexts like Japan.
We designed a virtual makeup probe to assist transgender individuals with passing, that is to be perceived as the gender they identify as.
We interviewed 15 individuals in Tokyo and found that in the right context and under strict conditions, AGR based systems could assist transgender passing.
arXiv Detail & Related papers (2021-03-08T04:43:10Z) - Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face
Recognition [51.856693288834975]
State-of-the-art deep networks implicitly encode gender information while being trained for face recognition.
Gender is often viewed as an important attribute with respect to identifying faces.
We present a novel Adversarial Gender De-biasing algorithm (AGENDA)' to reduce the gender information present in face descriptors.
arXiv Detail & Related papers (2020-06-14T08:54:03Z) - Multi-Dimensional Gender Bias Classification [67.65551687580552]
Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text.
We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
arXiv Detail & Related papers (2020-05-01T21:23:20Z) - Text-mining forma mentis networks reconstruct public perception of the
STEM gender gap in social media [0.0]
Textual forma mentis networks (TFMNs) are glass boxes introduced for extracting, representing and understanding mindsets' structure.
TFMNs were applied to the case study of the gender gap in science, which was strongly linked to distorted mindsets by recent studies.
arXiv Detail & Related papers (2020-03-18T13:39:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.