# Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media

arXiv: https://arxiv.org/abs/2405.06145 · HTML: https://mlsanity.com/paper/2405.06145/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 5 of 5)

- [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)
- [Social media mining for toxicovigilance of prescription medications: End-to-end pipeline, challenges and future work](https://mlsanity.com/paper/2211.10443/) (2022)
- [Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media](https://mlsanity.com/paper/2212.12454/) (2022)

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

- [Do LLMs Surpass Encoders for Biomedical NER?](https://mlsanity.com/paper/2504.00664/) (2025)
- [ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition](https://mlsanity.com/paper/2502.11198/) (2025)
- [Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models](https://mlsanity.com/paper/2508.06504/) (2025)
- [When Curiosity Signals Danger: Predicting Health Crises Through Online Medication Inquiries](https://mlsanity.com/paper/2509.11802/) (2025)
- [Opioid Named Entity Recognition (ONER-2025) from Reddit](https://mlsanity.com/paper/2504.00027/) (2025)
- ["Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessness](https://mlsanity.com/paper/2508.13187/) (2025)
- [Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset](https://mlsanity.com/paper/2508.19467/) (2025)
- [Large language models can disambiguate opioid slang on social media](https://mlsanity.com/paper/2603.10313/) (2026)
- [Curation and Extraction of Drug-Related Entities from Reddit Platform](https://mlsanity.com/paper/2605.26445/) (2026)

## Abstract (arXiv)

Substance use disorders (SUDs) are a growing concern globally, necessitating enhanced understanding of the problem and its trends through data-driven research. Social media are unique and important sources of information about SUDs, particularly since the data in such sources are often generated by people with lived experiences. In this paper, we introduce Reddit-Impacts, a challenging Named Entity Recognition (NER) dataset curated from subreddits dedicated to discussions on prescription and illicit opioids, as well as medications for opioid use disorder. The dataset specifically concentrates on the lesser-studied, yet critically important, aspects of substance use--its clinical and social impacts. We collected data from chosen subreddits using the publicly available Application Programming Interface for Reddit. We manually annotated text spans representing clinical and social impacts reported by people who also reported personal nonmedical use of substances including but not limited to opioids, stimulants and benzodiazepines. Our objective is to create a resource that can enable the development of systems that can automatically detect clinical and social impacts of substance use from text-based social media data. The successful development of such systems may enable us to better understand how nonmedical use of substances affects individual health and societal dynamics, aiding the development of effective public health strategies. In addition to creating the annotated data set, we applied several machine learning models to establish baseline performances. Specifically, we experimented with transformer models like BERT, and RoBERTa, one few-shot learning model DANN by leveraging the full training dataset, and GPT-3.5 by using one-shot learning, for automatic NER of clinical and social impacts. The dataset has been made available through the 2024 SMM4H shared tasks.