# Decoding the Narratives: Analyzing Personal Drug Experiences Shared on Reddit

arXiv: https://arxiv.org/abs/2406.12117 · HTML: https://mlsanity.com/paper/2406.12117/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 63 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [Want To Reduce Labeling Cost? GPT-3 Can Help](https://mlsanity.com/paper/2108.13487/) (2021)
- [Towards Making the Most of ChatGPT for Machine Translation](https://mlsanity.com/paper/2303.13780/) (2023)
- [DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4](https://mlsanity.com/paper/2303.11032/) (2023)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://mlsanity.com/paper/2310.15638/) (2023)
- [The price of debiasing automatic metrics in natural language evaluation](https://mlsanity.com/paper/1807.02202/) (2018)
- [Putting ChatGPT's Medical Advice to the (Turing) Test](https://mlsanity.com/paper/2301.10035/) (2023)
- [Creating Training Sets via Weak Indirect Supervision](https://mlsanity.com/paper/2110.03484/) (2021)
- [Distill or Annotate? Cost-Efficient Fine-Tuning of Compact Models](https://mlsanity.com/paper/2305.01645/) (2023)

## Built on by (most cited first, 9 of 9)

- [From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis](https://mlsanity.com/paper/2501.06101/) (2025)
- [Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models](https://mlsanity.com/paper/2408.07873/) (2024)
- [Like a Therapist, But Not: Reddit Narratives of AI in Mental Health Contexts](https://mlsanity.com/paper/2601.20747/) (2026)
- [Responsible Evaluation of AI for Mental Health](https://mlsanity.com/paper/2602.00065/) (2026)
- [Reddit After Roe: A Computational Analysis of Abortion Narratives and Barriers in the Wake of Dobbs](https://mlsanity.com/paper/2603.22566/) (2026)
- [From #Dr00gtiktok to #harmreduction: Exploring Substance Use Hashtags on TikTok](https://mlsanity.com/paper/2501.16123/) (2025)
- [Large language models can disambiguate opioid slang on social media](https://mlsanity.com/paper/2603.10313/) (2026)
- [The cognitive, affective, and behavioral expression of self-stigma among people who use drugs in online substance use communities](https://mlsanity.com/paper/2606.25143/) (2026)
- [Stigma and Support in Online Sexual Violence Narratives on Reddit](https://mlsanity.com/paper/2608.11433/) (2026)

## Abstract (arXiv)

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.