SocialCompUW
/

youtube-opioid-myth-detect-M5

@@ -1,199 +1,87 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- opioid-use-disorder
+- opioid
+- myth
+- misinformation
+- youtube
+license: mit
+language:
+- en
+metrics:
+- f1
+- accuracy
+base_model:
+- microsoft/deberta-v3-base
+pipeline_tag: text-classification
 ---
+# MYTHTRIAGE: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform
+This repository contains *one of eight* lightweight models accompanying the EMNLP 2025 paper [MYTHTRIAGE: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform](https://arxiv.org/abs/2506.00308). MythTriage is a scalable pipeline for detecting opioid use disorder (OUD) myths on YouTube, enabling large-scale analysis and informing moderation and health interventions.
+## Overview
+MythTriage is designed to automatically evaluate and classify YouTube videos for opioid use disorder myths. The triage pipeline uses lightweight models (fine-tuned DeBERTa-v3-base, as shown in this repository) for routine cases and defers harder ones to state-of-the-art, but costlier large language models (GPT-4o) to provide robust, cost-efficient, and high-performing detection of opioid use disorder myths on YouTube. For more information, please read our paper.
+MythTriage detects and classifies 8 categories of prevalent opioid use disorder myths recognized by major health organizations and validated by clinical experts. This repository contains the fine-tuned [DeBerta-v3-base model](https://huggingface.co/microsoft/deberta-v3-base) trained to detect one of eight categories of opioid use disorder myths in YouTube videos, namely: **M5: Physical dependence or tolerance is the same as addiction.**
+## Model Description & Datasets
+Given the YouTube video metadata (e.g., title, description, transcript, tags), the model will predict one of three numeric labels with respect to the myth (e.g., *M5: Physical dependence or tolerance is the same as addiction*): opposing the myth (0), neither (1), and supporting the myth (2).
+The video dataset used to train and evaluate the model is available at the [Github link here](https://github.com/hayoungjungg/MythTriage). As part of our distillation process, this model was trained on GPT-4o-generated synthetic labels on ~1.4K videos and then evaluated on ~300 gold-standard videos labeled by clinical experts. Additional details are provided in the paper.
 ## How to Get Started with the Model
+To get started, you should initialize the model using AutoTokenizer and AutoModelForSequenceClassification classes. For the AutoTokenizer, please use the tokenizer from [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) and set "use_fast" parameter to False, the max_len to 1024, padding to "max_length," and truncation to True.For the AutoModelForSequenceClassification, set the model to this repository and the "num_labels" parameter to 3.
+Next, with a YouTube video dataset with metadata, please concatenate each video's title, description, transcripts, and tags in the following manner:
+input = 'VIDEO TITLE: ' + title + '\nVIDEO DESCRIPTION: ' + description + '\nVIDEO TRANSCRIPT: ' + transcript + '\nVIDEO TAGS: ' + tags
+Thus, each video in your dataset should have its input metadata formatted in the structure above. Finally, run the input into a tokenizer and feed the tokenized input into the model to obtain one of three predicted labels. Use the logit function to obtain the label:
+_, pred_idx = outputs.logits.max(dim=1)
+## Training Hyperparameters
+During training, we conducted a grid search over learning rates (5e-6, 1e-5, 1e-6), weight decays (5e-4, 1e-4, 5e-5), and data balancing strategies (none, upsampling, class-weighted loss). Other hyperparameters include:
+- OPTIMIZER: Adam optimizer with cross-entropy loss function
+- BATCH_SIZE = 8
+- NUM_EPOCHS = 20
+- MIN_SAVE_EPOCH = 2
+The synthetic dataset of 1.4K videos was split 80:20 in training (N=1173) and validation sets (N=293). The 310 gold-standard dataset labeled by clinical experts served as the test set. The model was fine-tuned on a single NVIDIA A40 GPU.
 ### Results
+The model achieved a macro F1-score of 0.60 on the gold-standard test set annotated by clinical experts.
+## Other Models for Opioid Use Disorder Myths Detection
+As part of MythTriage, we finetuned eight lightweight models, each trained to detect a specific opioid use disorder myth. Below, we link the detection model corresponding to each myth:
+- M1: Agonist therapy or medication-assisted treatment (MAT) for OUD is merely replacing one drug with another [(LINK)](https://huggingface.co/SocialCompUW/youtube-opioid-myth-detect-M1)
+- M2: People with OUD are not suffering from a medical disease treatable with medication from a self-imposed condition maintained through the lack of moral fiber  [(LINK)](https://huggingface.co/SocialCompUW/youtube-opioid-myth-detect-M2)
+- M3: The ultimate goal of treatment for OUD is abstinence from any opioid use (e.g., Taking medication is not true recovery)  [(LINK)](https://huggingface.co/SocialCompUW/youtube-opioid-myth-detect-M3)
+- M4: Only patients with certain characteristics are vulnerable to addiction [(LINK)](https://huggingface.co/SocialCompUW/youtube-opioid-myth-detect-M4)
+- M5: Physical dependence or tolerance is the same as addiction [(LINK)](https://huggingface.co/SocialCompUW/youtube-opioid-myth-detect-M5)
+- M6: Detoxification for OUD is effective [(LINK)](https://huggingface.co/SocialCompUW/youtube-opioid-myth-detect-M6)
+- M7: You should only take medication for a brief period of time [(LINK)](https://huggingface.co/SocialCompUW/youtube-opioid-myth-detect-M7)
+- M8: Kratom is a non-addictive and safe alternative to opioids [(LINK)](https://huggingface.co/SocialCompUW/youtube-opioid-myth-detect-M8)
+# Citation
+If you used this model or the dataset in the Github in your research, please cite our work at:
+```bibtex
+@misc{jung2025mythtriagescalabledetectionopioid,
+      title={MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform},
+      author={Hayoung Jung and Shravika Mittal and Ananya Aatreya and Navreet Kaur and Munmun De Choudhury and Tanushree Mitra},
+      year={2025},
+      eprint={2506.00308},
+      archivePrefix={arXiv},
+      primaryClass={cs.CY},
+      url={https://arxiv.org/abs/2506.00308},
+}
+```