The J Healthcare Initiative is a non-profit organization that does projects to tackle the overdose crisis. In this project we address the pervasive issue of hate rhetoric, stigmatizing language, and misinformation in media coverage related to drug users and harm reduction through machine learning/AI. This project seeks to create a comprehensive analysis of current media practices and develop guidelines for responsible reporting. The goal is to ensure that media outlets are held accountable for their portrayal of drug users, promoting a more accurate and compassionate narrative. By doing so, the project will contribute to reducing stigma and fostering a more informed public discourse.

Objective: Develop a basic tool to analyze sentiment and language use in media articles about drug use and harm reduction.

Suggested timeline

Python Script for Media Analysis (25 hours)

• Develop a script that:
• Scrapes articles from 2-3 selected news websites
• Performs basic sentiment analysis
• Identifies key terms related to drug use and harm reduction
• Flags potentially stigmatizing language

Small Dataset of Analyzed Articles (10 hours)

• Collect and analyze 50-100 recent articles
• Store results in a structured format (e.g., CSV or JSON)

Summary Report (15 hours)

• Brief overview of the project and methodology
• Key findings from the analysis
• 2-3 data visualizations (e.g., sentiment distribution, word clouds)
• Short list of common stigmatizing terms found

Basic Web Interface (10 hours)

• Simple Flask or Streamlit app that:
• Allows users to input an article URL or text
• Runs the analysis script on the input
• Displays basic results (sentiment score, flagged terms)

Key objectives include:

- Analyzing existing media content for instances of hate rhetoric and misinformation.

- Identifying patterns and common issues in media representation of drug users.

- Developing a set of guidelines for responsible and accurate reporting.

- Proposing strategies for media accountability and public awareness.

The project can result in the following deliverables. It is NOT mandatory to provide all of these deliverables, they are suggestions based on our objectives.

Media Analysis Report:

• Detailed findings from the AI/ML analysis of media content
• Identification of patterns in hate rhetoric and misinformation
• Quantitative and qualitative data on media representation of drug users

Media Guidelines Handbook:

• Best practices for responsible reporting on drug use and harm reduction
• Examples of stigmatizing language to avoid and preferred alternatives
• Tips for accurate and compassionate storytelling

Interactive Online Dashboard:

• Real-time tracking of media sentiment and language use
• Visualizations of trends in media coverage over time
• Searchable database of analyzed media content

Media Accountability Scorecard:

• Rating system for media outlets based on their adherence to guidelines
• Regular updates on improvements or regressions in reporting quality

Public Awareness Campaign Materials:

• Infographics highlighting key issues in media representation
• Social media content kit for disseminating findings
• Short video series explaining the impact of stigmatizing language

AI/ML Model and Documentation:

• The trained machine learning model for ongoing media analysis
• Technical documentation for model use and maintenance

Quarterly Progress Reports:

• Updates on project milestones and achievements
• Emerging trends and patterns identified in the analysis

Media Training Workshop Curriculum:

• Materials for educating journalists and editors on responsible reporting
• Case studies and practical exercises

• A project mentor will be available for 10 hours total, spread across the project duration.
• Mentor will provide initial guidance, answer questions, and offer feedback at key milestones.
• Weekly 30-minute check-ins to ensure the learner is on track and address any challenges.
• Curated list of news sources focusing on drug-related coverage.