A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product
- URL: http://arxiv.org/abs/2410.01633v1
- Date: Wed, 2 Oct 2024 15:04:21 GMT
- Title: A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product
- Authors: Madhusudan Basak, Omar Sharif, Sarah E. Lord, Jacob T. Borodovsky, Lisa A. Marsch, Sandra A. Springer, Edward Nunes, Charlie D. Brackett, Luke J. ArchiBald, Sarah M. Preum,
- Abstract summary: Buprenorphine is one of the key FDA-approved medications for Opioid Use Disorder.
Despite its popularity, individuals often report various information needs regarding buprenorphine treatment on social media platforms like Reddit.
We propose a theme-based framework to curate and analyze large-scale data from social media to characterize self-reported treatment information needs.
- Score: 1.4599176517017673
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Background: One of the key FDA-approved medications for Opioid Use Disorder (OUD) is buprenorphine. Despite its popularity, individuals often report various information needs regarding buprenorphine treatment on social media platforms like Reddit. However, the key challenge is to characterize these needs. In this study, we propose a theme-based framework to curate and analyze large-scale data from social media to characterize self-reported treatment information needs (TINs). Methods: We collected 15,253 posts from r/Suboxone, one of the largest Reddit sub-community for buprenorphine products. Following the standard protocol, we first identified and defined five main themes from the data and then coded 6,000 posts based on these themes, where one post can be labeled with applicable one to three themes. Finally, we determined the most frequently appearing sub-themes (topics) for each theme by analyzing samples from each group. Results: Among the 6,000 posts, 40.3% contained a single theme, 36% two themes, and 13.9% three themes. The most frequent topics for each theme or theme combination came with several key findings - prevalent reporting of psychological and physical effects during recovery, complexities in accessing buprenorphine, and significant information gaps regarding medication administration, tapering, and usage of substances during different stages of recovery. Moreover, self-treatment strategies and peer-driven advice reveal valuable insights and potential misconceptions. Conclusions: The findings obtained using our proposed framework can inform better patient education and patient-provider communication, design systematic interventions to address treatment-related misconceptions and rumors, and streamline the generation of hypotheses for future research.
Related papers
- Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit [13.075510201220274]
Opioid use disorder (OUD) is a leading health problem that affects individual well-being as well as general public health.
Online communities for recovery and support were formed on different social media platforms.
We study natural language questions asked in the context of OUD-related discourse on Reddit.
arXiv Detail & Related papers (2025-04-10T18:02:24Z) - Opioid Named Entity Recognition (ONER-2025) from Reddit [5.641312824886231]
Social media platforms like Reddit provide vast amounts of unstructured data that offer insights into public perceptions, discussions, and experiences related to opioid use.
This study leverages Natural Language Processing (NLP), specifically Opioid Named Entity Recognition (ONER-2025), to extract actionable information from these platforms.
First, we created a unique, manually annotated dataset sourced from Reddit, where users share self-reported experiences of opioid use via different administration routes.
Second, we detail our annotation process and guidelines while discussing the challenges of labeling the ONER-2025 dataset.
Third, we analyze key linguistic challenges, including slang, ambiguity, fragmented
arXiv Detail & Related papers (2025-03-28T20:51:06Z) - Enhancing Depression Detection via Question-wise Modality Fusion [47.45016610508853]
Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs.
We propose a novel Question-wise Modality Fusion framework trained with a novel Imbalanced Ordinal Log-Loss function.
arXiv Detail & Related papers (2025-03-26T12:34:34Z) - Decoding the Narratives: Analyzing Personal Drug Experiences Shared on Reddit [1.080878521069079]
This study aims to develop a multi-level, multi-label classification model to analyze online user-generated texts about substance use experiences.
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.
arXiv Detail & Related papers (2024-06-17T21:56:57Z) - Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study [4.769236554995528]
We propose a retrieval-augmented generation architecture for medical question answering on emerging issues associated with health-related topics.
Our framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data.
Our framework achieves comparable median scores in terms of relevance, length, hallucination, coverage, and coherence when evaluated using GPT-4 and Nous-Hermes-2-7B-DPO.
arXiv Detail & Related papers (2024-05-29T20:56:52Z) - A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis [48.84443450990355]
Deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations.
We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled from different hospitals or data confounded by demographic variables such as sex, race, etc, in the context of chest X-rays and skin lesion images.
Taking inspiration from medical training, we propose giving deep networks a prior grounded in explicit medical knowledge communicated in natural language.
arXiv Detail & Related papers (2024-05-23T17:55:02Z) - Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media [6.138126219622993]
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
arXiv Detail & Related papers (2024-05-09T23:43:57Z) - Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary
Task Integration [54.76511683427566]
This research introduces a novel multimodal method for classifying skin lesions, integrating smartphone-captured images with essential clinical and demographic information.
A distinctive aspect of this method is the integration of an auxiliary task focused on super-resolution image prediction.
The experimental evaluations have been conducted using the PAD-UFES20 dataset, applying various deep-learning architectures.
arXiv Detail & Related papers (2024-02-16T05:16:20Z) - Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts [26.161892748901252]
We present a corpus of 2500 opioid-related posts from various subreddits labeled with six different phases of opioid use.
For every post, we annotate span-level explanations and crucially study their role both in annotation quality and model development.
arXiv Detail & Related papers (2023-11-15T16:05:55Z) - Critical Behavioral Traits Foster Peer Engagement in Online Mental
Health Communities [28.17719749654601]
We introduce BeCOPE, a novel behavior encoded Peer counseling dataset comprising over 10,118 posts and 58,279 comments sourced from 21 mental health-specific subreddits.
Our analysis indicates the prominence of self-criticism'' as the most prevalent form of criticism expressed by help-seekers, accounting for a significant 43% of interactions.
We highlight the pivotal role of well-articulated problem descriptions, showing that superior readability effectively doubles the likelihood of receiving the sought-after support.
arXiv Detail & Related papers (2023-09-04T14:00:12Z) - Med-Flamingo: a Multimodal Medical Few-shot Learner [58.85676013818811]
We propose Med-Flamingo, a multimodal few-shot learner adapted to the medical domain.
Based on OpenFlamingo-9B, we continue pre-training on paired and interleaved medical image-text data from publications and textbooks.
We conduct the first human evaluation for generative medical VQA where physicians review the problems and blinded generations in an interactive app.
arXiv Detail & Related papers (2023-07-27T20:36:02Z) - MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining
Three-Sequence Cardiac Magnetic Resonance Images [84.02849948202116]
This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS)
MyoPS combines three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020.
The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation.
arXiv Detail & Related papers (2022-01-10T06:37:23Z) - CREATe: Clinical Report Extraction and Annotation Technology [53.731999072534876]
Clinical case reports are written descriptions of the unique aspects of a particular clinical case.
There has been no attempt to develop an end-to-end system to annotate, index, or otherwise curate these reports.
We propose a novel computational resource platform, CREATe, for extracting, indexing, and querying the contents of clinical case reports.
arXiv Detail & Related papers (2021-02-28T16:50:14Z) - MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware
Medical Dialogue Generation [86.38736781043109]
We build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG.
We propose two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation.
Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset.
arXiv Detail & Related papers (2020-10-15T03:34:33Z) - Health, Psychosocial, and Social issues emanating from COVID-19 pandemic
based on Social Media Comments using Natural Language Processing [8.150081210763567]
The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives.
Social media data can reveal public perceptions toward how governments and health agencies are handling the pandemic.
This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data.
arXiv Detail & Related papers (2020-07-23T17:19:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.