From Stigma to Support: A Parallel Monolingual Corpus and NLP Approach for Neutralizing Mental Illness Bias (2023)

Abstract: Negative attitudes and perceptions towards mental illness continue to be pervasive in our society. One of the factors contributing to and reinforcing this stigma is the usage of language that is biased against mental illness. Identifying biased language and replacing it with person-first, neutralized language is a first step towards eliminating harmful stereotypes and creating a supportive and inclusive environment for those living with mental illness. This paper presents a novel Natural Language Processing (NLP) system that aims to automatically identify biased text related to mental illness and suggest neutral language replacements without altering the original text’s meaning. Building on previous work in the field, this paper presents the Mental Illness Neutrality Corpus (MINC) comprising over 5500 mental illness-biased text and neutralized sentence pairs (in English), which is used to fine-tune a CONCURRENT model system developed by Pryzant et al. (2020). After evaluation, the model demonstrates high proficiency in neutralizing mental illness bias with an accuracy of 98.7%. This work contributes a valuable resource for reducing mental illness bias in text and has the potential for further research in tackling more complex nuances and multilingual biases.

## introduction globally, 970 million people are currently living with mental illness. each year, 14.3% of deaths, or approximately 8 million people, are caused by mental disorders. depression affects over 300 million people worldwide, affecting people of all demographic and socioeconomic backgrounds. anxiety disorders are almost as common, affecting 284 million people worldwide @xcite . during the first year of the covid-19 pandemic, incidences of depression and anxiety increased by an astounding 25% @xcite . in addition to depression and anxiety disorders, suicidal ideation, bipolar disorder, autism spectrum disorder (asd), substance use disorder, and eating disorders such as bulimia and anorexia nervosa fall under the umbrella of mental illness @xcite . mental illnesses have traditionally been one of the most stigmatized health conditions. stigma and derogatory language about mental illness can be found everywhere, from casual conversations to media, even among medical professionals. language can be used to reinforce stigma or fight it. guidelines for responsible reporting by the media emphasize how language used in media can either encourage help-seeking behavior or inadvertently lead to suicide contagion (reporting on suicide, 2023). as an example, the phrase "committed suicide" is commonly used but has been replaced by the preferred "died by suicide" to avoid an association with "committing (a murder)" and demonstrate compassion through word choice @xcite . in casual conversation, mental health diagnostic terms are frequently used to describe a nonmedical event, such as the weather as "bipolar" or a situation as "insane". although seemingly harmless, misusing diagnostic terms can lead to misunderstandings about mental illness by undermining the severity of mental illness and reinforcing negative stereotypes @xcite . biased language affects how those with mental illnesses view themselves, how others treat them, and whether they seek help @xcite . people with mental illnesses, particularly ## mental illness neutrality corpus (minc) this paper introduces the mental illness neutrality corpus (minc) foot_0 , a novel parallel monolingual (specifically, english) corpus of mental illnessbiased text. this dataset is comprised of 5500+ mental illness-biased text, neutralized sentencepairs, and metadata. to construct the minc, several language guides foot_1 were referenced to compile a list of biased expressions and suggested text replacements of appropriate and respectful word choices. in addition to general terms describing mental illness, the corpus contains biased text describing substance use and eating disorders, which fall under the umbrella of mental illness as defined by several of the language guides mentioned above. to create sentences with biased text, chatgpt (openai, 2023) was prompted to pull real-world ways to combat discrimination with compassionate language, canadian centre on substance use and addiction's overcoming stigma through language: a primer, and "280 labels used to stigmatize people with mental illness" @xcite . after referencing literature on a list of commonly used biased text about mental illness, four categories of mental illness language bias were identified: each biased sentence is annotated in minc into one of these four categories (refer to ## approach this project employs the concurrent system proposed by @xcite and fine-tunes it using minc. this concurrent model architecture consists of two different modules, a detection module, and an editing module. the detection module aims to identify which word in the sequence is likely to be biased. it is a neuralsequence tagger that estimates pi, or the chance that a word is biased using the equation: ## training using the new minc, the concurrent system @xcite is fine-tuned. the minc data was split 85% training data and 15% testing data, with roughly 4120 sentences used for training data and roughly 730 sentences used for testing data. the fine-tuning process was implemented using pytorch and the adam optimizer with a learning rate 5e-5. batch size of 16 and all vectors of length @xmath0. gradient clipping with a maximum gradient norm of 3 was used and a dropout probability of 0.2 for the inputs of each lstm cell. the bert model was initialized using the bert-based-uncased pre-trained parameters @xcite . the other parameters were randomly initialized on the range [-0.1, 0.1]. after pre-training using the neutral text, the concurrent model was fine-tuned using the training data in addition to 710 sentences of neutral data for 20 epochs. the training time was approximately 3 hours, using the apple m1 chip. ## conclusion bias against mental illness is pervasive in our culture, frequently appearing as biased language in the media or casual conversation. although bias can be nuanced, implied, or unconscious, language biased against mental illness has a negative impact on those living with mental illness. identifying and reducing bias is crucial to reducing prejudice and helping those with mental illness seek and obtain the needed treatment. the proposed models in this study were highly proficient in providing appropriate neutralized suggestions for reducing subjective bias for the biased sentences generated by chatgpt. this paper presents the annotated corpus of mental illness biased text (minc). the minc is a novel monolingual parallel corpus generated by chatgpt from real-world text and trained on data from a wide variety of sources such as news media, social media, wikipedia, books, personal websites, etc. human intervention was necessary for annotation and review of grammatical errors. several language guides for journalists and writers were consulted to obtain a list of commonly used biased terms and phrases and replacements that were respectful and compassionate towards those with mental illness. this paper is a first step towards reducing bias in language describing mental illness, but further study should tackle more complex text such as multi-word, multilingual, and cross-sentence bias, as well as nuances and implicit language, taking into consideration that language, slang in particular, is ever-evolving. also worth noting is the minc is entirely in english. additional work to study language bias and applying our model to non-english languages would be a logical next step. language is a complex, ever-evolving field of study. while nlp is increasingly sophisticated, it has yet to replace human language cognition completely. however, using nlp models to reduce bias in real-world text is a significant step toward addressing and lessening mental illness bias in our society. given the substantial negative impact of biased language used to describe those with mental illness, creating more sophisticated detection models should be a high priority.