Domestic Abuse Risk Detection from Reddit Posts

A research project evaluating potential life-threatening severity in romantic relationships based on user-submitted posts in the r/relationship_advice subreddit.

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🧠 Objective

To assess the risk level posed to women in their relationships, as described in Reddit posts.
Risk levels are defined as follows:

• Low: No evident danger.
• Medium: Presence of warning signs.
• High: Multiple warning signs and recurring harmful patterns.

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📊 Dataset

• Source: Reddit (r/relationship_advice)
• Size: 300 manually annotated posts
• Annotation:
  • Each post was reviewed by 3–5 annotators.
  • Around 20 questions per post.
  • Questions grouped as:
    • Subjective (e.g., isolation from friends)
    • Objective (e.g., relationship duration)
    • Open-ended

Annotation guidelines were inspired by:

• i-Risk
• Michal Sela Forum – Warning Signs

Risk level examples

🖊️ Annotation

Annotation was performed using a Streamlit app (app.py) hosted on a remote server. The app interface allowed annotators to view Reddit posts and respond to a predefined set of subjective, objective, and open-ended questions.

• Hosted via port forwarding for secure remote access (ssh -L).
• Each post was presented along with ~20 annotation questions.
• Annotators selected a risk level (Low, Medium, High) based on guideline-aligned criteria.
• Responses were automatically saved to a structured backend (e.g., CSV or database).

The app facilitated fast iteration cycles and ensured annotation consistency across multiple annotators. Annotations were stored in a structured format for easy access and analysis.

• Annotation Process:
  • Annotators were trained on the guidelines and provided with examples.
  • Posts were randomly assigned to annotators.
  • Each annotator provided their risk assessment and comments.
  • Annotations were reviewed for consistency and accuracy.

Visualization of the Annotation Interface

Annotation Process

Questionnaire Analysis

• The questionnaire was designed to capture various aspects of the relationship.
• We analyzed the questionnaire responses to identify patterns and correlations with the risk levels assigned by annotators.

🤖 Model Setup

1. Classification Tasks

• Multiclass: low / medium / high
• Binary: low vs. medium+high

2. Models

• Classical ML classifiers, context-free (RF, LR)
• Bert-based models (RoBERTa)
• Combined Embeddings - Bert + Questionnaire
• GPT-based evaluation via prompt engineering

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🧪 GPT Prompt Engineering

Prompt Objective: Instruct GPT to assess relationship risk using behavioral cues, abuse indicators, and mental health risks (e.g., suicidal ideation, aggression, drug/alcohol use).

Example Prompt Excerpt:

Analyze the following Reddit post written by a female discussing her relationship with a male. Carefully assess the potential life-threatening risk...

Posts were formatted into prompt-completion pairs, with model outputs compared to annotated labels.

Visualization of the Prompt Engineering Process

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✅ Evaluation

• Compared simple word-feature models with GPT outputs.
• Assessed model sensitivity to risk patterns across posts.
• Explored model agreement with human annotations.

Evaluation Metrics

• Macro F1
• Weighted F1
• Auc

🔭 Next Steps

• Collect additional high-quality, real-world data.
• Collaborate with domestic abuse professionals for validation.
• Develop context-aware deep learning models.
• Evaluate GPT robustness across diverse post types.