Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models (2024)

Abstract: Stigma is a barrier to treatment for individuals struggling with substance use disorders (SUD), which leads to significantly lower treatment engagement rates. With only 7% of those affected receiving any form of help, societal stigma not only discourages individuals with SUD from seeking help but isolates them, hindering their recovery journey and perpetuating a cycle of shame and self-doubt. This study investigates how stigma manifests on social media, particularly Reddit, where anonymity can exacerbate discriminatory behaviors. We analyzed over 1.2 million posts, identifying 3,207 that exhibited stigmatizing language related to people who use substances (PWUS). Of these, 1,649 posts were classified as containing directed stigma towards PWUS, which became the focus of our de-stigmatization efforts. Using Informed and Stylized LLMs, we developed a model to transform these instances into more empathetic language.Our paper contributes to the field by proposing a computational framework for analyzing stigma and de-stigmatizing online content, and delving into the linguistic features that propagate stigma towards PWUS. Our work not only enhances understanding of stigma’s manifestations online but also provides practical tools for fostering a more supportive environment for those affected by SUD.

## introduction warning: this paper includes language and content that may be offensive or triggering. every day, people struggling with substance use disorders (sud) face a pervasive and often hidden enemy: stigma. this stigma, often deeply ingrained in societal attitudes, can act as a significant barrier to treatment and recovery. in fact, only approximately 7% of people living with an sud receive any form of treatment (substance abuse and mental health services administration, 2023), with stigma reported as a major barrier (centers for disease con- type statement original i have no empathy for drug addicts. i had friends and family who have struggled with the "disease". everyone knows what happens when you start, and you usually end up dead. many of my old friends have become addicts and i don't understand especially the ones with kids. de-stigmatized i find it difficult to empathize with individuals facing substance use challenges. i had friends and family who encountered these difficulties. it's widely acknowledged that there are risks involved from the outset, and the outcomes are often heartbreaking. several of my old friends have dealt with these challenges, and it's particularly perplexing to me when they are parents. de-stigmatized version generated with our informed + stylized model using gpt-4 removed stereotypes and harmful context while preserving the tone (stigma is in red, destigmatized counterparts is in blue). social media platforms like reddit have emerged as important spaces for community discussions @xcite . however, the anonymity provided by these environments sometimes exacerbates stigmas, leading to discrimination. people suffering from sud often encounter derogatory comments, judgment, or misinformation online @xcite , which can reinforce self-stigma and stop them from seeking help. the spread of stigmatizing attitudes on social media can also influence public opinion, further perpetuating the stereotypes and prejudices against those with sud @xcite . as a result, despite the potential for support, the digital space can mirror and magnify the very societal stigmas it has the power to dismantle, affecting individuals' mental health and recovery processes adversely @xcite . the widespread stigma surrounding sud requires urgent and innovative solutions. leveraging technology and social media, we can develop empathetic, supportive interventions that fight against this stigma @xcite . while research has explored mental health conversations and public perceptions on social media @xcite , there remains a significant gap in efforts to destigmatize language in these discussions. addressing this gap is crucial for fostering a more understanding and supportive environment for those affected by sud. our work explores this opportunity and examines how stigmatizing language manifests in online communities and what solutions can be applied for de-stigmatizing such narratives ( to address these research questions, we collected over 1.2 million posts from non-drug-related subreddits, identifying 3,207 posts containing stigmatizing language towards people who use substances (pwus). leveraging large language models (llms), we developed a framework to characterize stigma based on conceptualization of @xcite (labeling, stereotyping, separation, status loss, and discrimination) and transform them into more empathetic versions, resulting in 1,649 de-stigmatized pairs. our analysis showed that stimulants and cannabis were the most frequently mentioned substances, with stigma more generally being associated with interpersonal relationships and moral judgments. human evaluations showed that our informed + stylized system using gpt-4 can reduce stigma while preserving the original tone and relevance. automatic evaluations further confirmed that our approach effectively reduced stigma while maintaining the stylistic and psycholinguistic properties of the original posts. our work makes several key contributions: (1) public release of a unique dataset of labeled stigmatizing posts; (2) demonstration of frameworks for de-stigmatizing text; and (3) exploration of the linguistic characteristics of stigma expressions towards pwus online. additionally, this study introduces innovative uses of llms for generating suggestions to mitigate potentially harmful language. ## data to achieve the study's objective of addressing stigmatizing language, we specifically focused on nondrug-related subreddits. this choice was made to capture how stigmatizing language manifests externally rather than within communities where members discuss their own experiences with drug use. within these communities, stigmatizing language is often directed towards oneself (e.g., "no one should hire a junkie like me, i'm useless") or describes situations where members felt stigmatized (e.g., "my co-workers stopped having lunch with me when they learned i've been to rehab twice") which differs from the external stigmatizing language we aim to address. by focusing on nondrug-related subreddits, we ensure that our analysis targets the perpetuation of harmful stereotypes by those outside the drug-using community. this methodological choice is informed by the need to differentiate between internal and external stigma, as highlighted in the literature on stigma (e.g., @xcite 's attributes of stigma). ## methodology to develop a stigma detection model and destigmatize texts, we first need to filter posts related to substance use. this is then followed by detection and de-stigmatization processes. figure 1 shows our study's overall pipeline. each step is detailed in the following sections. ## discussion our study set out to address two main research questions: (rq1) how does stigmatizing language manifest in non-drug-related reddit communities when discussing sud, and what are the underlying factors that contribute to such expressions? and (rq2) how can we leverage llms to effectively de-stigmatize language, and what factors influence the success of this process? stigma also stems from personal connections (rq1). our findings revealed a complex landscape of stigma within non-drug-related online communities where discussions about substance use often become entangled with interpersonal relationships and ingrained societal biases -particularly towards specific substances, namely stimulants (e.g., methamphetamine) and cannabis (e.g., "weed," "pot"). the frequent mentions of these substances within a stigmatizing context may reflect societal concerns about their visibility and impact, aligning with our topic modeling results (appendix c the online nature of these interactions presents a duality of stigma manifestations that is important to understand when developing any intervention. while anonymity might offer a shield for individuals to express stigmatizing views they might suppress offline, it could also create a space for open dialogue and support. the disinhibition afforded by online platforms could lead to more candid discussions about sud, potentially challenging stigma through shared experiences and mutual understanding. however, it may also create a space for misinformed judgments and harmful stereotypes, as anonymity can reduce accountability. when considering de-stigmatization efforts, any digital intervention should consider the social actors in addition to the social constructs (e.g., hospitals, employers). this would be considerably important in collectivist communities (e.g., indian or middle eastern) where stigma towards family members with an sud (i.e., affiliate stigma) may prevent families from providing the necessary medical support to their loved ones and ultimately delaying treatment @xcite . llms can be guided by explanation and stylistic information (rq2). in our de-stigmatization efforts, we intentionally avoided providing the llms with a rigid definition of "de-stigmatized." instead, we adopted a more nuanced approach, drawing inspiration from the principles of "constitutional ai" @xcite and prior work on text detox-ification and bias reduction using llms @xcite @xcite . we focused on explaining why specific phrases might be problematic and instructed the model to address these issues, constitutionally, while preserving the original style. for instance, to tackle separation, the llms were guided to draw equivalences between individuals with sud and those without, emphasizing shared humanity. labeling was addressed by replacing derogatory terms like "junkie" with person-centered language like "person with a substance use disorder," (substance abuse and mental health services administration, 2023) mitigating the over-generalization tendencies of llms. stereotyping and discrimination were handled by re-framing generalizations and removing any implications of discrimination, promoting a more empathetic understanding of individuals struggling with sud. most de-stigmatized does not mean most pragmatic. while the baseline model removes stigmatizing language, it often does so at the expense of nuance and context. for instance, evaluators noted that the baseline model sometimes "terribly misunderstood the post," resulting in generic or insincere responses that failed to capture the original poster's intent. this highlights the importance of removing stigma and preserving the authenticity and emotional tone of the original message. our findings emphasize the importance of striking a balance between promoting empathetic language and providing overly refined language, which might trivialize the experiences of individuals with sud or avoid addressing the root causes of stigma. ## conclusion this study investigated the manifestations of stigma towards pwus in four popular nondrug-related subreddits (r/unpopularopinion, r/offmychest, r/nursing, r/medicine). we identified 3,207 posts containing one of three main types of stigma (self, structural, and directed). given the contextual nuance of self and structural stigma, we focused our efforts on de-stigmatizing instances of directed stigma (@xmath0,649). experimenting with three different models and two different llms (gpt-4 and llama), the model that used the conceptualization of stigma @xcite , few-shot examples, and the original post's stylistic profile generated the most faithful and appropriate destigmatized texts. our exploration of llm-based de-stigmatization demonstrates the potential of these models to transform harmful language into more empathetic expressions while emphasizing the importance of preserving authenticity and the original poster's voice. while our focus has been on sud stigma, the insights and methodologies presented here have broader implications for understanding and addressing stigma related to other marginalized groups. future work could explore the role of misinformation in perpetuating stigma and leverage external knowledge bases (e.g., drugbank) to develop more informed and effective de-stigmatization strategies. by integrating these approaches, we can create a more supportive and inclusive online environment for individuals affected by stigma, ultimately promoting understanding, empathy, and recovery.