Sociocultural Considerations in Monitoring Anti- LGBTQ + Content on Social Media (2024)

Abstract: The purpose of this paper is to ascertain the influence of sociocultural factors (i.e., social, cultural, and political) in the development of hate speech detection systems. We set out to investigate the suitability of using open-source training data to monitor levels of anti-LGBTQ+ content on social media across different national-varieties of English. Our findings suggests the social and cultural alignment of open-source hate speech data sets influences the predicted outputs. Furthermore, the keyword-search approach of anti-LGBTQ+ slurs in the development of open-source training data encourages detection models to overfit on slurs; therefore, anti-LGBTQ+ content may go undetected. We recommend combining empirical outputs with qualitative insights to ensure these systems are fit for purpose.

## introduction the proliferation of hate speech on social media platforms continues to negatively impact lgbtq+ communities @xcite . as a consequence of anti-lgbtq+ hate speech, these already minoritised and marginalised communities may experience digital exclusion and barriers to access in the form of the digital divide @xcite . there have been considerable developments within the field of natural language processing (nlp) in response to this social issue @xcite , with most of the methodological advancements in this area being made in the last three decades @xcite . while much of hate speech research has focused on documentation and detection, there has been little attention on how these approaches can be applied across different social, political, or linguistic contexts @xcite . just as the appropriateness of swear words is highly contextually variable depending on language and culture @xcite , hate speech in the form of anti-lgbtq+ hate speech is often predicated by social, cultural, and political attitudes towards diverse gender and sexualities. with minimal literature beyond just a system development context, we set out to investigate the suitability of implementing open-source anti-lgbtq+ hate speech system on real-world sources of social media data. this paper makes two contributions: firstly, we show the predicted outputs from classification models can be transformed into various time series data sets to monitor the rate and volume of anti-lgbtq+ hate speech on social media. secondly, we argue that social, cultural, and linguistic bias introduced during the data collection phase has an impact on the suitability of these approaches. ## methodology as discussed in section 1.1, hate speech detection research needs to undergo a paradigmatic shift in order to truly enable positive social impact, social good, and social benefit potential. the main purpose of this paper is to ascertain the influence of sociocultural factors (i.e., social, cultural, and political) in the development of hate speech detection systems. our research questions are as follows: rq1 can we use open-source hate speech training data to monitor anti-lgbtq+ hate speech in real world instances of social media? and; rq2 how do the social, cultural, and linguistic contexts of open-source training data impact on the suitability of anti-lgbtq+ hate speech detection? in order to address rq1, we compare and contrast two anti-lgbtq+ hate speech detection systems. we provide an in depth description of the data sources in section 2.1 and our system development pipeline in section 2.2. once we develop the detection systems, we apply the detection systems on real-world samples of social media data to monitor anti-lgbtq+ hate speech across different geographic dialects. we opted for a mixed-methods approach to address this emergent area of enquiry. this is because rq2 can only be addressed qualitatively as we consider the suitability of the detection systems and the sociocultural relevance of the predicted outputs. we will address rq2 in the discussion (section 4); however, we have provided relevant sociolinguistic, cultural, and political information in section 2.3 to contextualise our discussion. ## results we dedicate the current section to describe the results of the second phase of our investigation. this phase involved applying the candidate models to automatically detect anti-lgbtq+ hate speech on real-world instances of social media data in english. firstly, we applied both anti-lgbtq+ hate speech detection models on the ten randomised monthly samples of social media language data from india using the same sampling methodology for other national-varieties of english. the results are shown in figure 1 . as expected, the ltedi r model predicted higher rates of anti-lgbtq+ hate speech; however, what was unexpected were the low number of predictions from the mlma r model. the narrow confidence intervals suggest little instability between the different samples and the predictions remained constant across samples. after validating our sampling methodology by visually inspecting the ten randomised monthly samples from india, we applied both models on random samples of inner-and outer-circle varieties of english. we compared the results of the detection models as visualised in figure 2 . these were consistent with our initial results. the rate of anti-lgbtq+ hate speech remained constant according to the mlma r model, while anti-lgbtq+ hate speech has increased over time based on a visual inspection of the results. of interest to our investigation, the mlma r model identified a higher proportion of anti-lgbtq+ hate speech in inner-circle varieties of english. we saw an inverse relationship with the ltedi r where we see a higher proportion of anti-lgbtq+ hate speech in outer-circle varieties of english. the wide confidence intervals of the ltedi r suggests greater between-variety instability in outer-circle varieties of english. we calculated the quarterly growth rates for each variety of english for the predictions from the ltedi r . we included the total number of predicted posts/tweets in our visualisation as shown in figure 3 . the growth rates allowed us to determine the growth rate for each variety of english independently. the results suggest the growth rate of predicted anti-lgbtq+ hate speech has remained stable over time. ## discussion the results from our study raises some interesting questions on the efficacy of these systems on real-world instances of social media data. with regards to the first research question, our transformerbased multiclass classification model enabled us to detect instances of anti-lgbtq+ hate speech from samples of georeferenced posts/tweets from x (twitter). we were able to manipulate the predicted outputs into different forms of time series as shown in figures 2 and 3 . the level of anti- lgbtq+ hate speech has maintained a constant rate of growth despite decreasing usership on the social media platform since the acquisition of x (twitter) by elon musk in 2022. the results suggest that anti-lgbtq+ hate speech on x (twitter) is indeed increasing in both rate and volume over time @xcite . when we compare the predicted results between the mlma r and the ltedi models, we can see significant differences between the two models. this is particularly obvious when we compare the predicted outputs in figures 1 and 2 , where the ltedi r model on average predicted 50 times more instances of anti-lgbtq+ hate speech than the mlma r . these was unexpected as the model evaluation metrics during model development suggested the mlma r model performed marginally better than the ltedi r . considering both the sampling methodology and the model development approaches were held constant between the models, we propose the differences we see in the predicted outputs is a result of the open-source training data. one challenge of applying multiclass classification models on unknown data is that there is no simple method to validate the results. this is because we do not have access to labelled training, development, and test sets to evaluate the model performance. we are therefore reliant on qualitative methods to validate the performance of our detection models. figure 4 is a visual representation of the word-token frequencies between the open-source training data (a) and the predicted anti-lgbtq+ hate speech (b) from the samples of posts/tweets. the most prominent word-token in the training data is faggot followed by dyke. this is not unexpected as these word-tokens (including twat) were used to identify instances of anti-lgbtq+ hate speech on x (twitter). counterintuitively, we did not see a similar distribution in the predicted outputs. with reference to figure 4 , the word-tokens with the highest frequency in the predicted output were not faggot or dyke, but sleep and gay. when we filtered for the keyword search terms in the samples, we found few instances across the varieties of english as shown in tables 4 and 5 . this is unexpected as the keyword search terms are highly prevalent in inner-circle varieties of spoken english (such as the united kingdom and ireland) @xcite . this is supported by the higher wordtoken frequencies in inner-circle varieties of english as shown in tables 4 and 5 . we attribute the infrequent occurrence of lgbtq+ slurs in direct response to x (twitter) rules which discourages hateful conduct on the platform. our analysis of the mlma r model suggests a relationship between the training data and the resulting detection model. incidentally, we also observe this bias towards inner-circle varieties of english in figure 2 where the mlma r is more inclined to identify more anti-lgbtq+ hate speech in innercircle than outer-circle varieties of english. this leads our discussion to the second research question where we determine how the social, cultural, and linguistic context impacts the efficacy of anti-lgbtq+ hate speech detection. although anti-lgbtq+ discourse is consistent across languages @xcite , slurs and swearwords are not @xcite . this form of cultural bias toward inner-circle varieties of english (or oversight of outer-circle varieties) introduced during the data collection process, raises questions on the suitability of the mlma r model in monitoring anti-lgbtq+ hate speech. as we determined the ltedi r model to be more culturally aligned with the south asian context, we initially predicted the ltedi model would be more appropriate for south asian contexts. however, the results suggest the ltedi r model as more fit for purpose in contrast to the mlma r model. not only do we observe high-congruency between the ltedi r model output and the outer-circle varieties of english as shown in figure 2 , the word-token frequencies between the training data (a) and the predicted outputs (b) in the ltedi appear to have a similar distribution as shown in figure 5 . curiously, both the training data and predicted output lack slurs. instead, we see word-tokens associated with community (e.g., people) and religion (e.g., bible, god, and adam possibly in reference to the abrahamic creation myth of adam and eve). this is unsurprising as anti-lgbtq+ legislation is often rooted in puritanical beliefs on morality @xcite . with reference to figure 3 , we observed a possible link between the increased growth rate with nationwide response to the covid-19 pandemic. once again this raises a question on the validity of the predicted outputs and whether the posts/tweets are anti-lgbtq+ or religious/spiritual in nature (or indeed, both). ## conclusion the findings from this current paper raises a number challenges in applying hate speech detection in a real-world context. even within national-varieties of english, we observed the impacts of social, cultural, and linguistic factors. for example, the ltedi r which was culturally aligned with indian english was more sensitive to outer circle varieties of english, while the mlma r model was slightly more sensitive to inner circle varieties of english. we conclude that monitoring anti-lgbtq+ hate speech with open-source training data is not problematic in itself; however, we must interpret these empirical outputs with qualitative insights to ensure these systems are fit for purpose. contexts in english. this study contributes to the efforts in mitigating harmful hate speech experienced by lgbtq+ communities. in our investigation, we combine methods from nlp, sociolinguistics, and discourse analysis to evaluate the effectiveness of anti-lgbtq+ hate speech detection. we recognise the importance of advocate and activist-led research in particular by members of under-represented and minoritised communities @xcite . the lead author acknowledges their positionality as an active advocate and a member of the lgbtq+ community @xcite . the lead author is familiar with anti-lgbtq+ discourse both in online and offline spaces and its harmful effects on members of the lgbtq+ communities. as discussed in section 5, we support the critique of @xcite for nlp researchers to reflect on the efficacy and suitability of hate speech detection models. the development of hate speech data sets impose a 'diversity tax' on already marginalised lgbtq+ communities. originally coined by @xcite , this refers to the unintentional burden placed on marginalised peoples to address inequities, exclusion, and inaccessibility particularly in a research context. nlp researchers need to work alongside key-stakeholders (e.g., affected communities, advocates, and activists) as well as social media platforms, non-profit organisations, and government entities to determine the solutions of this social issue. the inclusion of unobfuscated examples of slurs, hate speech, and offensive language towards lgbtq+ communities is a deliberate attempt to initiate the process of reclaiming and reappropriating some anti-lgbtq+ slurs in nlp research. currently, there are limited best practice guidelines on the obfuscation of profanities in nlp research @xcite . @xcite theorised that anti-lgbtq+ slurs are used to stigmatise violations of social norms. re-appropriating these stigmatising labels can enhance what were once devalued social identities @xcite . this process of 'cleaning' and 'detoxifying' slurs is also a process of resistance and to reclaim power and control @xcite . we argue that within context of social media research giving unwarranted attention to slurs ignores the root of this social issue: hate speech expresses hate @xcite . many social media platforms have already put in place procedures to censor sensitive word-tokens; however, social media users continue to adopt innovative linguistic strategies such as voldermorting (van der nagel, 2018) and algospeak @xcite to contravene well-meaning moderation and censorship algorithms. our results suggest hate speech training data sets do not identify the full breadth of hateful content on social media. this paper does not include human or animal participants. furthermore, we abide by the data sharing rules of x (twitter) and posts/tweets with identifiable personal details will not be shared publicly. the authors have no conflicts of interests to declare.