Decoding the Narratives: Analyzing Personal Drug Experiences Shared on R eddit (2024)

Abstract: Online communities such as drug-related subreddits serve as safe spaces for people who use drugs (PWUD), fostering discussions on substance use experiences, harm reduction, and addiction recovery. Users’ shared narratives on these forums provide insights into the likelihood of developing a substance use disorder (SUD) and recovery potential. Our study aims to develop a multi-level, multi-label classification model to analyze online user-generated texts about substance use experiences. For this purpose, we first introduce a novel taxonomy to assess the nature of posts, including their intended connections (Inquisition or Disclosure), subjects (e.g., Recovery, Dependency), and specific objectives (e.g., Relapse, Quality, Safety). Using various multi-label classification algorithms on a set of annotated data, we show that GPT-4, when prompted with instructions, definitions, and examples, outperformed all other models. We apply this model to label an additional 1,000 posts and analyze the categories of linguistic expression used within posts in each class. Our analysis shows that topics such as Safety, Combination of Substances, and Mental Health see more disclosure, while discussions about physiological Effects focus on harm reduction. Our work enriches the understanding of PWUD’s experiences and informs the broader knowledge base on SUD and drug use.

## introduction warning: this paper includes language and content that may be offensive or triggering. for people who use drugs (pwud), social platforms like reddit serve as invaluable spaces for open discussion and community support. such platforms enable pwud to engage in conversations and share experiences, facilitated by the anonymity and community solidarity that reddit provides (figure 1 ). despite reddit's ability to foster connection through shared experiences @xcite , the perspectives of pwud are frequently marginalized in important decision-making processes, particularly for those who depend exclusively on online communities due to the significant stigma associated with seeking help from traditional services @xcite . analyzing how pwud communicate about their experiences on online platforms offers important insights into their narratives, highlighting their information-seeking behaviors and the diverse needs specific to this population @xcite , revealing the real-life challenges and perspectives of individuals dealing with substance use disorders (sud) @xcite . existing literature in this domain primarily focuses on classifying mental health experiences like depression @xcite or drug-related events like abuse @xcite or overdose risk @xcite . personal lived experiences of pwud remain largely unexplored, highlighting the need for effective methodologies to understand and analyze diverse drug experiences shared online. our study aims to bridge this gap by exploring a range of substance use experiences shared online, from recovery to general discussions, through the development of a taxonomy and model for classifying these narratives on reddit. insights from this study will not only expand our understanding of how pwud navigate information seeking and support mechanisms, thereby laying the groundwork for harm reduction strategies and more nuanced, effective interventions, but also help in identifying and understanding the diverse experiences and characteristics of individuals most impacted by drug misuse @xcite . to understand the experiences of pwud, we first developed a fine-grained taxonomy of online drug experiences comprising three levels: connection (which highlights the information-seeking and sharing behaviors of users), subject (lived sud-related experiences such as dependency and recovery), and objectives (detailing the aspects shared within posts related to sud experiences). after testing and evaluating our new taxonomy, we annotated posts and developed multi-level and multi-label classification models using different baseline and state-of-the-art approaches. these models were used to categorize different types of personal drug experiences shared in posts from four prominent drug-related subreddits r/cocaine, r/opiates, r/stims, r/benzodiazepines. these subreddits were selected as they are the largest subreddits that represent substances identified as most commonly abused @xcite . the results of our analysis show that gpt-4 outperformed other models and more accurately labeled classes across each of the three levels. further exploration of the labeled posts showed that online posts are more inquisitive in nature and discuss themes relating to desired or undesired effects of the substance and/or how to consume them. especially in the context of recovery-related posts, users emphasize themes of nurturant support, relapse, and safety. moreover, our results show that discussions around dependency frequently cover the effects and methods of ingestion. psycholinguistic analysis revealed that posts that contain topics such as safety, combination of substances, and effects tend to share personal experiences and exhibit a higher prevalence of language indicative of harm reduction efforts, as well as personal disclosures on family and social support systems. our study makes several contributions. firstly, it introduces a new taxonomy for the classification of personal drug experiences. additionally, we have developed a human-annotated dataset comprising 500 reddit posts related to drug use, which showcases the wide range of personal drug experiences discussed in user-generated content. we also demonstrate the capability for automatic classification of personal drug experiences across three levels and multiple classes. lastly, our work includes an analysis of the narratives surrounding substance use, sud, and recovery based on selfdisclosed user experiences. this work enriches the understanding of pwud's experiences and informs the broader knowledge base on sud and drug use. ## multi-level lived experience detection we used 400 annotated posts to train a series of multi-label models, each customized for different levels of classification granularity: connection, subject, and objective. minimal pre-processing was applied to maintain data integrity, which included converting text to lowercase, removing urls, expanding contractions, and eliminating stopwords. we evaluate the models' performance on the test set consisting of 100 posts, using precision, recall, and f1 score metrics. figure 2 shows the overall pipeline of this study. baseline models. we implemented four baseline machine learning models to benchmark our drug experience classification system. we chose classic, feature-based algorithms for their interpretability and widespread use: logistic regression (logr), random forest (rf), support vector machines (svm), and k-nearest neighbors (knn). to provide a comprehensive evaluation, we trained and tested each model with two distinct feature sets: tf-idf vectors and bert @xcite pre-trained [cls] token embedding. this approach allowed us to understand the impact of feature representation on model performance. in addition, we integrated openai's ada-002 architecture into our approach. leveraging the capabilities of these llm, we generated high-level features and trained our selected classic models. transformer-based models. we used pretrained language models from hugging face @xcite to extract rich contextual features. specifically, bert-base-uncased @xcite , roberta-base @xcite , debertabase @xcite , and biobert-v1.1 @xcite were used. all models were fine-tuned and trained using a cross entropy loss function for 5 epochs and a learning rate of 2e -5. during training, we optimized the model with adamw optimizer @xcite . recognizing the limitations of large-scale annotation, we explored few-shot learning techniques. we employed setfit @xcite , one of the most efficient few-shot architectures, leveraging sentbert @xcite features. this choice aligns with current research advocating for few-shot models in situations where extensive annotation is impractical or costly @xcite . to optimize performance, we finetuned the model using the cosine similarity loss function, for 2 epochs with a learning rate of 2e -5 and a batch size of 8. ## usefulness of our models in this study, we introduce a new taxonomy to better understand the lived experiences of pwud online and analyze these experiences in usergenerated texts on reddit. our study builds upon existing literature that employed qualitative analysis to uncover elements of social support within drug-focused reddit communities @xcite @xcite @xcite . however, we apply computational methods to conduct this exploration, a novel approach compared to previous studies that have primarily concentrated on binary classifications @xcite or other aspects of mental health @xcite @xcite . our experiments demonstrate significant performance differences between classical machine learning, state-of-the-art transformer-based models, and llms for multi-label classification of drugrelated posts. llms outperformed other models in detecting diverse aspects of lived experience disclosures, underscoring their potential for larger-scale investigations into personal narratives of drug use. current u.s. addiction recovery frameworks often fail to consider the lived experiences of individuals with sud @xcite , assuming universal access to professional guidance-a reality not shared by many affected. online communities fill this gap, offering acceptance, understanding, and validation not always present in professional settings, thus becoming crucial for those lacking healthcare access @xcite . they enable discussions on substance use and recovery, playing a vital role for individuals isolated from traditional healthcare resources @xcite . our study aims to address these knowledge gaps by detailing the experiences of pwud, enhancing the ability of experts to offer more comprehensive and personalized support. our results show a balanced mix of users seeking (inquisition) and sharing (disclosure) information, aligning with prior work @xcite . analyzing selfdisclosed experiences within the posts indicates a minor portion discussing recovery experiences, touching on nurturant support, relapse, and safety. this finding is expected as recovery subreddits like r/opiatesrecovery, and r/benzorecovery, host more detailed recovery discussions. prior research identified nurturant support themes of recovery and addiction in the r/drugs subreddit @xcite . our study builds on this, offering deeper insights into the nuanced discussions of recovery and dependency, as shown in this post: among posts that disclosed a dependency experience (@xmath0, most contained themes of effects and methods of ingestion, resonating with the concept of action-facilitating support identified in the r/drugs community @xcite : "... i was on opiates. i started at ten-mg hydro twice a day for 2-3 months, after being directed to a pain doctor they were able to move me to 10mg/325 percocet 4x a day since july...with all the horrible stigma surrounding opiates, i'm anxious to talk to my doctor about bumping or changing my meds...my tolerance has gone...i sometimes notice wd symptoms when i wait too long" this relationship confirms the value of online forums as spaces for sharing and obtaining information on substance use and recovery, highlighting our study's relevance. moreover, examining conversations about ingestion methods and effects can aid harm reduction. sharing insights on safer practices, dosages, and possible side effects can help users reduce substance use risks. ## the language of personal drug experiences we applied our most effective model, gpt4 'i + d + e', to extend our analysis to 1,000 randomly selected posts from our dataset for psycholinguistic analysis of pwud's lived experiences. to ensure a more representative sample and increase statistical power, we augmented the pre-annotated set of 500 posts with 1,000 randomly selected posts. this resulted in a comprehensive analysis of 1,500 posts across three dimensions: connection, subject, and objective. detailed results can be found in appendix d. we found that 47 liwc categories were significantly associated with inquisition posts (@xmath1.05) overall, while six were significantly associated with recovery posts. inquisition vs. disclosure. as expected, posts seeking information (inquisition) used significantly more question marks (qmark). however, this pattern was less pronounced in posts discussing safety (0.94), combination of substances (0.84), and mental health (0.86), suggesting users disclose more information about these topics. notably, we observed 1.16 times greater authenticity in inquisition posts compared to disclosure posts. posts discussing withdrawal experiences were less inquisitive, indicating a preference for sharing personal experiences rather than seeking information on this sensitive topic. higher usage of liwc categories such as friend, conflict, affiliation further support this interpretation ( the use of prosocial language (e.g., "care", "help") was 2.54 times more frequent in posts inquiring about dependency concerns. this may suggest that individuals seeking information about dependency may frame their inquiries in a way that elicits support and empathy. we found prosocial language also more prevalent in disclosure posts related to combination of substances (0.46), indicating that discussions of polysubstance use may involve a greater degree of mutual support. while the use of language to establish clout was generally more prevalent in disclosure posts (0.89), likely reflecting a desire to establish credibility when sharing personal experiences, this pattern was particularly notable in discussions of methods of ingestion (0.69), suggesting that individuals sharing experiences related to drug use methods may be especially motivated to present themselves as knowledgeable and authoritative. finally, while our analysis revealed significant differences in the use of relig and food categories, further investigation highlighted the limitations of liwc limitations in capturing the nuances of drugrelated discourse. these terms often appear in metaphorical contexts within drug discourse (e.g., "god," "hell," "cook," "bake"), underscoring the need for specialized linguistic tools tailored to the unique language of online drug forums. ## conclusion this study aims to identify how individuals discuss their personal drug experiences online. using a deductive-inductive approach, we developed a taxonomy to assess user-generated posts, including their intended connections (inquisition or disclosure), subjects (e.g., recovery, dependency, other), and specific objectives (e.g., relapse, quality, safety, legality). we then employed this taxonomy to annotate 500 randomly sampled posts from a dataset we created, consisting of posts from four subreddits: r/opiates, r/benzodiazepines, r/stims, and r/cocaine. we used this data to train three sets of classifiers: (1) baseline models, (2) transformerbased deep learning models, and (3) llm-based models including gpt-3.5 turbo, gpt-4, and mixtral, an open-source llm. our data analysis shows that posts are more inquisitive and predominately revolve around effects and methods of ingestion. our classification results show that gpt-4 with instruction, definition, and examples ('i +d+ e') in prompts outperformed other models, whereas the deberta-based transformer model had the best performance among the non-llm models. after applying our best-performing model to an additional 1,000 randomly selected posts, we used liwc to analyze the linguistic differences between posts labeled as inquisition vs. disclosure and recovery vs. dependency. this analysis revealed that inquisition posts significantly used more authenticity-related language, while disclosure posts emphasized personal sharing, especially in sensitive topics like withdrawal. the analysis also highlighted the varied use of prosocial, clout, and metaphorical language across different discussion themes, providing deeper insights into the psychosocial dynamics of online drug-related discourse. these findings provide insight into the intricate language of drug use discussions, highlighting potential indicators of sud or recovery initiation. our results underscore how online forums provide crucial support and safety planning, demonstrating the value of computational analysis in understanding health-related online communities and informing sud treatment and recovery interventions.