A Cross-Lingual Study of Homotransphobia on T witter (2023)

Abstract: We present a cross-lingual study of homotransphobia on Twitter, examining the prevalence and forms of homotransphobic content in tweets related to LGBT issues in seven languages. Our findings reveal that homotransphobia is a global problem that takes on distinct cultural expressions, influenced by factors such as misinformation, cultural prejudices, and religious beliefs. To aid the detection of hate speech, we also devise a taxonomy that classifies public discourse around LGBT issues. By contributing to the growing body of research on online hate speech, our study provides valuable insights for creating effective strategies to combat homotransphobia on social media.

## introduction despite significant advancements in laws and societal attitudes surrounding lgbt rights around the world, homotransphobia, which refers to the hatred and discrimination towards individuals who identify as lesbian, gay, bisexual, or transgender, remains a pervasive phenomenon across diverse cultures @xcite . the prevalence and visibility of hate speech toward lgbt individuals have escalated in the age of social media, further exacerbating the challenge of combating such discriminatory behavior. recent surveys reveal that a substantial proportion of lgbt individuals have fallen prey to online attacks through homotransphobic messages, posing a serious threat to their well-being. 2,3 the fight against online homotransphobic speech can be aided by natural language processing (nlp) techniques. automatic hate speech detection systems, in particular, have the potential to reduce the spread of harmful language flagging such content for removal. however, the task of detecting homotransphobic speech is far from simple, given the multifaceted nature of this phenomenon. in order to accurately identify it, detection methods must take into account cross-lingual factors and recognize the subtle nuances in how this form of intolerance manifests itself in different cultures. despite its social relevance and harmful effects, this phenomenon has received little attention from nlp researchers compared to other types of hate speech, such as aggression @xcite , misogyny @xcite @xcite , and racism @xcite . one of the main challenges for developing effective homotransphobic detection models is the scarcity of annotated data in this domain @xcite @xcite and the negative bias of nlp models regarding lgbt individuals @xcite . in this paper, we conduct a cross-lingual study to investigate public discourse surrounding lgbt issues on twitter, to identify areas where homotransphobic speech persists. to achieve this, we analyze a vast corpus of tweets in seven languages using topic modeling and sentiment analysis. these techniques have been extensively used in observational studies @xcite @xcite . we aim to offer a nuanced understanding of the emergence of different themes of homotransphobic speech across different languages. additionally, we propose a taxonomy for categorizing this discourse, establishing a foundation for the development of more effective homotransphobic speech detection models. we maintain the project repository at https: //github.com/milanlproc/crosslin gual-analysis-homotransphobia. ## data we examined seven languages -german, english, french, italian, norwegian, spanish, and portuguese -and collected tweets containing lgbt keywords. these included both neutral terms (e.g., "gay") and derogatory slurs (e.g., "f*ggot"). to ensure that our list of keywords is comprehensive and representative of the different linguistic contexts, we recruited native speakers for each language in our study. moreover, we selected individuals who are familiar with the lgbt community and its terminology. where possible, we included multiple native speakers per language from diverse backgrounds and regions. using twitter's historical api, we retrieved around 1.5 million tweets from may to september 2022, which coincided with pride month celebrations that we expected to increase discussions on lgbt issues. we sampled 25,000 tweets for each language, except for norwegian, which had fewer tweets. to ensure that our collection reflects a realistic distribution, we compared it with an estimate of the total number of tweets posted for each language in a week during the same period. the number of tweets for each language is summarized in ## methodology we extracted 10 topics for each language, using contextualized topic modeling (ctm) @xcite . we then developed a taxonomy to characterize lgbt public discourse, consisting of five broad categories and several subcategories, described in to devise this taxonomy we employed a multiround process of annotation. first, we conducted a review of relevant literature from social science studies to identify common themes @xcite @xcite @xcite . next, we collected personal accounts from lgbt individuals, with a particular focus on their perception of lgbt public discourse. based on these findings, we created an initial draft of the taxonomy that grouped the themes into categories. to ensure that the framework was as accurate as possible, the annotators used it to devise initial labels for the topics emerged from ctm. in cases where inconsistencies were found, we refined the taxonomy further, breaking down each category into subcategories. tweets that were discovered to touch on subjects unrelated to lgbt issues were grouped into a distinct category named "other / irrelevant". for instance, tweets that were selected using a keyword with multiple meanings, some of which were not related to the lgbt community, were placed in this category. we then used a pre-trained multilingual sentiment analysis classifier @xcite to analyze the attitudes expressed in the tweets. here, we employ sentiment as a soft proxy for homotransphobia, because no multilingual detection models have been developed to date and cross-lingual hate speech detection methods does not transfer across different targets and languages @xcite . it is important to note that the sentiment of a tweet is not a perfect measure for identifying hate speech, since it can potentially capture other phenomena, overlook some forms of hate speech, and misinterpret benign language as hateful due to contextual nuances and subtleties of natural language. however, category subcategory topics example gender and sexuality gender roles and sexual identity societal expectations on gender / sex trans women are not women language and terminology meaning of lgbt words you can't say f*ggot pornography pornographic content click to see this s*ssy prejudice cultural stereotypes homotransphobic beliefs gays will burn in hell slurs and stigmatization insults using anti-lgbt words you're such a f*ggot sociopolitical influences politics and policy lgbt rights f*ck the equality act events and organizations promoting lgbt visibility can't wait for pride! legal issues legal challenges / advocacy efforts sign this petition for gay rights... cultural representation representation in media lgbt portrayal in media the main character is gay anti-lgbt language in sports homotransphobic slurs in sports your team plays like f*ggots other / irrelevant topics irrelevant to lgbt issues i smoked a f*g yesterday ## results in this section we describe the main findings by category, which are summarised in ## discussion through our research, we have gained insight into the widespread use of homotransphobic language in all the languages we examined: despite hate speech detection systems are implemented, our findings suggest that there remains a significant amount of homotransphobic language. this highlights the pervasive nature of this issue and underscores the need for more targeted efforts to combat this phenomenon. we found significant differences across languages. for instance, we found that in norwegian, the derogatory term "f*ggot" ("bøg"), appeared in only eight tweets across the entire dataset. this stands in stark contrast to the other languages we studied, where derogatory terms were more prevalent. it is clear that addressing this issue requires approaches that account for these cultural differences. our findings have shed light on the higher incidence of homotransphobic language in religious and conservative cultural contexts, specifically in french and italian tweets. we observed a link of this trend to misinformation, particularly to health issues such as monkeypox and vaccines. in addition, we observed the effects of politics on homotransphobic language: countries with less comprehensive lgbt-safety legislation had higher rates of such language use, underscoring the importance of effective frameworks to protect lgbt rights. interestingly, we found that derogatory language tends to be directed more frequently toward transgender rather than homosexual individuals in some of the languages, such as spanish. this highlights the need for interventions that specifically address this issue, rather than using a broad approach. ## conclusion we conducted a cross-lingual analysis of seven languages, examining how public discourse on twitter frames lgbt individuals and issues. our findings indicate that homotransphobic language continues to be prevalent despite the implementation of automatic hate speech detection models. additionally we contributed a taxonomy for categorizing homotransphobic discourse, which can serve as a valuable tool to create datasets, as well as defining lgbt-related topics for analysis. by shedding light on the ways in which different cultures and languages frame lgbt issues, we hope that our study will contribute to ongoing efforts to promote acceptance and equality for all individuals.