The Language of Recovery: Modeling Communication Dynamics and OUD Recovery with LLMs

Murtaza Nasir, Assistant Professor
Department of Finance, Real Estate and Decision Sciences

April 2, 2025

The Context: OUD Crisis & Online Support

• The Opioid Use Disorder (OUD) crisis remains a critical public health challenge (e.g., CDC, 2024).
• Many face barriers like lack of trust or access in traditional healthcare settings (Olsen & Sharfstein, 2014; Krawczyk et al., 2022).
• Online Communities (e.g., Reddit's r/OpiatesRecovery) provide vital peer support and relatable experiences (Chancellor et al., 2019).

The Challenge & Our Approach

• Understanding communication dynamics & their impact in these large, nuanced online forums is difficult.
• Large Language Models (LLMs) enable fine-grained analysis of communication style, emotion, and stigma at scale (Goel et al., 2024).
• Our Goal: Leverage LLMs to model these dynamics and link them to OUD recovery trajectories.

The Digital Lifeline: OHCs & OUD Support

• Crucial online spaces for peer-to-peer connection and shared lived experience in recovery.
• Offer anonymity & accessibility, reducing stigma often felt in traditional settings (e.g., Krawczyk et al., 2022; Naslund et al., 2016).
• Provide vital social support, fostering understanding and a sense of belonging crucial for OUD recovery (e.g., Chancellor et al., 2019).

Theoretical Lens: Communication Accommodation Theory (CAT)

• We inherently adapt our communication style (language, tone, pace) to others (Giles & Ogay, 2007).
• Motivations: Manage social distance, gain approval, increase understanding, build rapport.
• Provides a framework to analyze how support is exchanged (or hindered) in OUD recovery forums.

Analyzing Communication Styles: Key CAT Dimensions

Using LLMs, we quantify how users adapt communication across key dimensions:

Guiding Research Question

• Central Focus: How does the way people communicate in online recovery forums influence their journey with OUD?
• LLM-Powered Lens: We use Large Language Models to precisely measure:
  • Communication Accommodation (CAT) strategies
  • Emotional expression & tone
  • Presence and nature of Stigma
  • Relapse-related narratives
• The Link: Do these measured communication features correlate with, or potentially predict, self-reported recovery outcomes (clean days, relapse events) on Reddit?

Further Explorations

Rich Data: Authentic Lived Experiences

• These forums offer a rich repository of authentic, user-generated text capturing lived experiences and support exchange (Saha et al., 2019).

Visualizing Recovery Journeys: Longitudinal Data

• Reddit data offers potential for longitudinal analysis by following users' interactions and self-reported status over time (e.g., Chancellor et al., 2019).

Example Trajectories: "Rocky but Resolute"

Methodology: Harnessing LLMs for Text Analysis

• Utilized locally hosted Large Language Models (LLMs) for deep, nuanced analysis of post-reply interactions. (Leveraging models like Llama, Qwen)
• Engineered detailed prompts ensuring structured JSON output. (Crucial for consistency & scale)
• This structured, prompt-driven approach provides methodological control over the feature extraction process (cf. Wei et al., 2022 on prompt engineering).

Methodology: Key Features Extracted via LLMs

Methodology: The Data Analysis Pipeline

LLM Feature Engineering - Example: CAT Analysis

• Result: Rich, quantifiable features characterizing interaction dynamics.
• Prompt Structure & Definitions:

   # Relevant snippet from the Python function:
   def analyze_cat(post, reply, subreddit):
       # ... (truncation code omitted for brevity) ...
       question = f"""You are an expert analyzing online communication patterns in Reddit posts from
       r/{subreddit}. Your task is to evaluate how a reply accommodates or differs from the original post
       using Communication Accommodation Theory (CAT).
  
       WHAT YOU'RE ANALYZING:
       A Reddit post and its direct reply. We want to understand how the replier adjusts their
       communication style in response to the original poster.
  
       SCORING SYSTEM:
       - Low: Minimal or no evidence of the characteristic
       - Moderate: Some clear evidence, but not consistently strong
       - High: Strong and consistent evidence throughout
       - For message length only: Short (1-2 sentences), Medium (3-5 sentences), Long (6+ sentences)
  
       KEY DIMENSIONS TO EVALUATE:
  
       1. CONVERGENCE (How much does the reply match the original post?)
           • Language Similarity:
               - HIGH: Uses very similar vocabulary, phrases, or jargon
               - MODERATE: Some matching language
               - LOW: Very different vocabulary choices
  
           • Tone and Style Matching:
               - HIGH: Matches the original post's formality/casualness, emotion, and style
               - MODERATE: Partially matches the tone
               - LOW: Completely different tone
  
       2. DIVERGENCE (How much does the reply differ from the original post?)
           • Language Dissimilarity:
               - HIGH: Deliberately uses different vocabulary and expressions
               - MODERATE: Some distinct language choices
               - LOW: Very few distinct language choices
  
           • Tone and Style Contrast:
               - HIGH: Deliberately different emotional tone or style
               - MODERATE: Somewhat different tone
               - LOW: Little contrast in tone
  
       3. INTERPERSONAL CONTROL
           • Topic Management:
               - HIGH: Introduces new topics or strongly redirects discussion
               - MODERATE: Some new elements while maintaining original topic
               - LOW: Strictly follows original topic
  
           • Assertiveness:
               - HIGH: Strong opinions, disagreement, or persuasion attempts
               - MODERATE: Some opinion expression
               - LOW: Minimal opinion expression
  
       4. DISCOURSE MANAGEMENT
           • Message Length: Short/Medium/Long
  
           • Coherence and Cohesion:
               - HIGH: Clear logical flow and strong connections
               - MODERATE: Generally understandable flow
               - LOW: Disjointed or unclear connections
  
           • Turn-taking:
               - HIGH: Strong engagement with original post's points
               - MODERATE: Some engagement
               - LOW: Minimal engagement
  
       5. INTERPRETABILITY
           • Clarity:
               - HIGH: Very clear and well-explained
               - MODERATE: Generally clear
               - LOW: Unclear or confusing
  
           • Relevance:
               - HIGH: Directly addresses post content
               - MODERATE: Somewhat related
               - LOW: Barely related or off-topic
  
       6. EMOTIONAL EXPRESSION
           • Emotional Content:
               - HIGH: Strong emotional language
               - MODERATE: Some emotional content
               - LOW: Minimal emotion
  
           • Emotional Reciprocity:
               - HIGH: Strongly mirrors original post's emotions
               - MODERATE: Some emotional matching
               - LOW: Different emotional tone
  
       Here are the Reddit posts to analyze:
  
       Original Post:
       {truncated_post}
  
       Reply:
       {truncated_reply}
  
       Return JSON with this exact structure:
       {{
           "convergence_language_similarity": "low/moderate/high",
           "convergence_tone_style_matching": "low/moderate/high",
           "divergence_language_dissimilarity": "low/moderate/high",
           "divergence_tone_style_contrast": "low/moderate/high",
           "interpersonal_control_topic_management": "low/moderate/high",
           "interpersonal_control_assertiveness": "low/moderate/high",
           "discourse_management_message_length": "short/medium/long",
           "discourse_management_coherence": "low/moderate/high",
           "discourse_management_turn_taking": "low/moderate/high",
           "interpretability_clarity": "low/moderate/high",
           "interpretability_relevance": "low/moderate/high",
           "emotional_expression_emotional_content": "low/moderate/high",
           "emotional_expression_emotional_reciprocity": "low/moderate/high"
       }}"""
           return question
                                   

Methodology: LLM-Powered Stigma Analysis

LLM Task: Deconstructing Stigma

Instructed LLMs to analyze post & reply text based on detailed definitions, performing these key tasks:

Methodology: From Raw Reports to Numeric Days

Users frequently anchor discussions in their recovery milestones:

Planned Analysis 1: Predicting Relapse Risk

• 🎯
  Goal: Predict user relapse risk over time using rich communication context.
• 🧠
  Method 1: Deep Text Understanding
  Generate nuanced text embeddings (e.g., BERT) for posts/replies.
  (Devlin et al., 2019)
• 📈
  Method 2: Modeling Temporal Dynamics
  Use LSTMs or Transformers on text embeddings + LLM features.
  (Captures how communication evolves towards relapse)
• ✨
  Why: Go beyond static features; capture time dependencies & subtle linguistic shifts predictive of relapse.

Planned Analysis 2: Estimating Causal Impact

• 🎯
  Goal: Estimate the causal effect of specific communication strategies (CAT, stigma) on recovery outcomes (days clean, time to relapse).
• ⚙️
  Methods: Advanced Causal Inference
  Explore Double Machine Learning (DML), Causal Forests, TMLE.
  (Chernozhukov et al., 2018; Athey et al., 2019)
• 🤖
  Leveraging: Combine causal methods with Deep Learning to handle high-dimensional LLM features & control for confounders (e.g., user history).
• ✨
  Why: Move beyond correlation – understand if and how much specific communication styles directly influence recovery trajectories.

Planned Analysis 3: Discovering Patterns & Themes

• 🎯
  Goal: Uncover latent structures, user archetypes, and discussion themes within the communication data.
• 🗺️
  Method 1: Visualize the Landscape
  Apply dimensionality reduction (UMAP, t-SNE) to embeddings/features.
  (McInnes et al., 2018) - Look for clusters (e.g., emotional styles, support needs).
• 🔍
  Method 2: Identify Key Topics
  Use Topic Modeling (LDA, Neural Models) on text, potentially stratified by outcome.
  (Blei et al., 2003) - Find themes associated with relapse vs. recovery.
• ✨
  Why: Gain qualitative insights, generate new hypotheses, identify distinct user subgroups or communication patterns not captured by supervised models.