Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment.Further, those in treatment are more likely to drop out if they experience higher levels of stigmatization. While related concepts of hate speech and toxicity, including those targeted toward vulnerable populations, have been the focus of automatic content moderation research, stigma and, in particular, people who use substances have not. This paper explores stigma toward PWUS using a data set of roughly 5,000 public Reddit posts. We performed a crowd-sourced annotation task where workers are asked to annotate each post for the presence of stigma toward PWUS and answer a series of questions related to their experiences with substance use. Results show that workers who use substances or know someone with a substance use disorder are more likely to rate a post as stigmatizing. Building on this, we use a supervised machine learning framework that centers workers with lived substance use experience to label each Reddit post as stigmatizing. Modeling person-level demographics in addition to comment-level language results in a classification accuracy (as measured by AUC) of 0.69 -- a 17% increase over modeling language alone. Finally, we explore the linguist cues which distinguish stigmatizing content: PWUS substances and those who don't agree that language around othering ("people", "they") and terms like "addict" are stigmatizing, while PWUS (as opposed to those who do not) find discussions around specific substances more stigmatizing. Our findings offer insights into the nature of perceived stigma in substance use. Additionally, these results further establish the subjective nature of such machine learning tasks, highlighting the need for understanding their social contexts.

翻译：对物质使用者（PWUS）的污名是寻求治疗的主要障碍。此外，经历较高程度污名化的治疗者更可能中途退出。虽然仇恨言论和毒性内容（包括针对弱势群体的内容）等相关概念已成为自动内容审核研究的焦点，但污名，特别是针对物质使用者的污名，尚未得到充分关注。本文基于约5000条Reddit公开帖子数据集，探讨了对物质使用者的污名现象。我们开展了一项众包标注任务，要求标注员标注每条帖子中是否存在对物质使用者的污名，并回答一系列与其物质使用经历相关的问题。结果表明，自身使用物质或认识物质使用障碍者的标注员更倾向于将帖子评为具污名性。基于此，我们采用以具有物质使用经验的标注员为中心的监督机器学习框架，将每条Reddit帖子标记为具污名性。结合个人层面的人口统计特征与评论层面的语言特征，模型分类准确率（以AUC衡量）达到0.69——比仅使用语言特征建模提高了17%。最后，我们探究了区分污名化内容的语言线索：非物质使用者认为关于他者化的语言（“人们”、“他们”）及“瘾君子”等术语具有污名性，而物质使用者（相对于非使用者）则认为围绕具体物质的讨论更具污名性。本研究揭示了物质使用领域中感知污名的本质特征，同时进一步证实了此类机器学习任务的主观性，凸显理解其社会背景的必要性。