What is the social benefit of hate speech detection research? A Systematic Review (2024)

Abstract: While NLP research into hate speech detection has grown exponentially in the last three decades, there has been minimal uptake or engagement from policy makers and non-profit organisations. We argue the absence of ethical frameworks have contributed to this rift between current practice and best practice. By adopting appropriate ethical frameworks, NLP researchers may enable the social impact potential of hate speech research. This position paper is informed by reviewing forty-eight hate speech detection systems associated with thirty-seven publications from different venues.

## introduction social impact is a conceptual model used to determine the practice and science of social good factoring: 1) social good domains (including diversity and inclusion; environmental justice and sustainability; and peace and collaboration); 2) unconventional systems of change; and 3) innovative technologies @xcite . indeed, one area of natural language processing (nlp) which seamlessly unites all three elements of social impact is hate speech detection @xcite . in the last three decades, we have seen an exponential growth into hate speech research with rapid developments in the last decade alone as a result of methodological advancement in nlp @xcite . the main contribution of nlp research in combating hate speech is through the development of hate speech detection training data sets. this is because hate speech detection is often treated as a text classification task and the development of hate speech detection systems follow a similar workflow: a) data set collection and preparation; b) feature engineering; c) model training; and lastly d) model evaluation @xcite . a systematic review of hate speech literature has identified over sixty-nine hate speech detection systems @xcite . however, these systems pose a number of ethical challenges and risks to the vulnerable communities they are meant to protect @xcite . as an area of research enquiry, hate speech research is highly productive. for example, the flagship publisher of computational linguistics and natural language processing research, acl anthology, returned 6,570 results for 'hate speech' as of june 2024. this number pales in comparison to the staggering 116,000 publications indexed by google scholar. while hate speech research has been purported as a valuable resource in policing anti-social behaviour online @xcite , some researchers are beginning to question the social benefits of proposed nlp solutions in combating hate speech @xcite . the efforts of nlp researchers are rarely used to combat hate speech. in a review of hate speech policies, the key players in this space were non-profit organisations, social media platforms, and government agencies @xcite . hate speech detection research rarely appear in policy documents. as an example, the most cited hate speech publication had 2,861 citations on google scholar @xcite , but only twice in overton -a database of policy documents and working papers for 188 countries. the absence of nlp research suggest that methodological innovations are of are incongruent with legal and ethical concerns of this social issue @xcite . nlp researchers do not seem to be concerned that their hate speech systems are not being widely applied or implemented. this is because the primary concern in hate speech research is poor model performance which is often attributed noisy training data @xcite . laaksonen et al. (2020) critiqued the 'datafication' of hate speech research has become an unnecessary distraction for nlp researchers in combating this social issue. 1 this is a well-attested issue in nlp research for positive social impact @xcite as a relatively new field of academic enquiry @xcite , there remains a paradigmatic rift between current practice and evidencebased best practice. @xcite expressed their concerns on the negative social impacts of nlp research. this is because nlp research was previously immune from research ethics as nlp approaches did not directly involve human subjects. nlp researchers are increasingly aware they are not immune from ethical dilemmas. as an example, recent work have identified racial bias in hate speech systems @xcite . if nlp researchers wish to enable the intended positive social impact of hate speech detection systems, then there must be a re-orientation of how the problem of hate speech detection is conceived from a methods-based problem towards collaborative solution @xcite . this view is shared by the broader field of nlp for social good whereby the needs of users and communities are centred over the methods @xcite . one proposed approach is to determine the responsibility of nlp solutions and system to consider its broader impact on target users and communities. ## analysis we retroactively apply the rnlp conceptual model to evaluate the ethical and responsible performance of hate speech systems. each system is rated on a three-point scale: where there is no evidence (not met), some evidence (partially met), and good evidence (met). while the rnlp evaluates an nlp system in its entirety, we restrict our analysis to the training data sets used to train these systems. as part of our systematic review, we only refer to publicly available publications (or in some instances, pre-prints) and associated data or metadata repository for evidence when evaluating each system. ## results a summary of the results from our systematic evaluation is presented in the remaining systems did not include a discussion of limitations at all. nineteen systems (39.6%) met principle 4: pri-vacy and security (p4) and twenty-one systems (43.8%) partially met this principle. the systems which met this principle published de-identified data with a small number stored securely with approval required. eight systems (16.7%) did not meet this principle which raises both ethical and legal concerns. thirty-nine systems (81.3%) met principle 5: reliability (p5) while nine systems (18.8%) partially met this principle. thirty-one systems (64.6%) did not meet principle 6: fairness (p6) as there were no responsible disclosures. the remaining systems (33.3%) partially met this principle with limited information about the annotators. over half (52.1%) of the systems met principle 7: interrogation (p7). lastly, the majority (95.8%) of systems did not meet principle 8: accountability (p8). ## discussion while the systematic review provides useful insights of hate speech detection systems from a structural perspective, it does not provide insights into systemic issues. we therefore organise our discussion using the deliberative framework on responsible innovation in ai @xcite to determine the broader ethical implications of the sample of hate speech detection systems as highlighted from our systematic review. ## conclusion while the systematic review cannot determine why there is a lack of engagement from key stakeholders of target groups and communities, the insights on how nlp researchers can improve ethical decision making in the development of hate speech detection systems. based on the systematic review, nlp researchers working in the field of hate speech detection are consistently meeting the principles of principle 5: reliability, principle 7: interrogation, and principle 4: privacy and security. the two principles which require the most attention are principle 8: accountability and principle 3: fairness. some of these ethical concerns may be addressed systemically and structurally through the adoption of ethical frameworks (such as buhmann and fieseler 2021 or @xcite ; however, true positive social benefits may only be achieved by working alongside target groups and communities most impacted by this social issue.