Decoding the Narratives: Analyzing Personal Drug Experiences Shared on Reddit

• URL: http://arxiv.org/abs/2406.12117v1
• Date: Mon, 17 Jun 2024 21:56:57 GMT
• Title: Decoding the Narratives: Analyzing Personal Drug Experiences Shared on Reddit
• Authors: Layla Bouzoubaa, Elham Aghakhani, Max Song, Minh Trinh, Rezvaneh Rezapour,
• Abstract summary: 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.
• Score: 1.080878521069079
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Causal Micro-Narratives [62.47217054314046]
  We present a novel approach to classify causal micro-narratives from text. These narratives are sentence-level explanations of the cause(s) and/or effect(s) of a target subject.
  arXiv  Detail & Related papers  (2024-10-07T17:55:10Z)
• Contrastive Learning with Counterfactual Explanations for Radiology Report Generation [83.30609465252441]
  We propose a textbfCountertextbfFactual textbfExplanations-based framework (CoFE) for radiology report generation. Counterfactual explanations serve as a potent tool for understanding how decisions made by algorithms can be changed by asking what if'' scenarios. Experiments on two benchmarks demonstrate that leveraging the counterfactual explanations enables CoFE to generate semantically coherent and factually complete reports.
  arXiv  Detail & Related papers  (2024-07-19T17:24:25Z)
• 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)
• "Hey..! This medicine made me sick": Sentiment Analysis of User-Generated Drug Reviews using Machine Learning Techniques [2.2874754079405535]
  This project proposes a drug review classification system that classifies user reviews on a particular drug into different classes, such as positive, negative, and neutral. The collected data is manually labeled and verified manually to ensure that the labels are correct.
  arXiv  Detail & Related papers  (2024-04-09T08:42:34Z)
• Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy [52.426623750562335]
  We introduce ToTER (Topical taxonomy Enhanced Retrieval) framework. ToTER identifies the central topics of queries and documents with the guidance of the taxonomy, and exploits their topical relatedness to supplement missing contexts. As a plug-and-play framework, ToTER can be flexibly employed to enhance various PLM-based retrievers.
  arXiv  Detail & Related papers  (2024-03-07T02:34:54Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Lived Experience Matters: Automatic Detection of Stigma on Social Media Toward People Who Use Substances [1.7378386177263254]
  Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment. This paper explores stigma toward PWUS using a data set of roughly 5,000 public Reddit posts.
  arXiv  Detail & Related papers  (2023-02-04T02:26:08Z)
• Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection [9.334701229573739]
  Adverse drug events (ADEs) are an important aspect of drug safety. Various texts contain a wealth of information about ADEs. Recent studies have applied word embedding and deep learning -based natural language processing to automate ADE detection from text. We propose a concept-aware attention mechanism which learns features differently for the different types of nodes in the graph.
  arXiv  Detail & Related papers  (2023-01-25T08:01:45Z)
• Clinical Named Entity Recognition using Contextualized Token Representations [49.036805795072645]
  This paper introduces the technique of contextualized word embedding to better capture the semantic meaning of each word based on its context. We pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) Explicit experiments show that our models gain dramatic improvements compared to both static word embeddings and domain-generic language models.
  arXiv  Detail & Related papers  (2021-06-23T18:12:58Z)
• TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial Study [3.9424051088220518]
  We propose the use of Deep Learning and Word Embeddings for identifying sixteen morbidity types within textual descriptions of clinical records. We have employed pre-trained Word Embeddings namely GloVe and Word2Vec, and our own Word Embeddings trained on the target domain.
  arXiv  Detail & Related papers  (2021-05-20T09:57:45Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)

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.