BBC Disability News Coverage Analysis

A quantitative analysis of disability representation patterns in BBC News' dedicated disability section (January-July 2025).

Academic Paper

Full Academic Paper (PDF)

Note: GitHub's PDF viewer removes clickable links. Please download the PDF for full functionality including clickable TOC and references

Key Findings

Analysis of 707 headlines from BBC's disability section reveals:

• 3.9:1 ratio between visible and invisible disability coverage
• Mental health receives 0.8% of coverage despite affecting 1 in 4 people
• SEND/Special Schools dominates at 17% of all coverage
• Diagonal pattern in co-occurrence matrix shows disabilities presented in isolation rather than as intersectional experiences

Methodology

This analysis employs a dual methodology:

1. Multi-category analysis: Measures thematic prevalence (articles can match multiple categories)
2. Exclusive categorization: Provides unique article distribution for statistical validity

The approach reveals that BBC disability coverage averages 1.42 category matches per article, yet the co-occurrence heatmap shows minimal intersection between different disability experiences in how stories are framed.

Dataset

• Source: Complete scrape of https://www.bbc.co.uk/news/disability
• Period: January 1 - July 29, 2025
• Size: 707 articles
• File: bbc-2025-07-29.csv

Usage

# Clone the repository
git clone https://github.com/[username]/bbc-disability-analysis
cd bbc-disability-analysis

Install requirements

pip install pandas matplotlib seaborn numpy

Run analysis

python bbc_analysis_v3.py

Replication Guide

Data Collection

1. Install Easy Scraper - One Click Web Scraper Chrome extension
2. Navigate to https://www.bbc.co.uk/news/disability
3. Configure columns to extract:
   • ssrcss-gfjuy9-Timestamp - Date/time (e.g., "16:02" or "15:16 28 July")
   • visually-hidden - Full publication timestamp with accessibility text
   • ssrcss-yjj6jm-LinkPostHeadline - Main headline text (this is what we analyze)
   • ssrcss-61mhsj-MetadataText - Location/region metadata
4. Scroll to load all articles for your target date range
5. Export as CSV using the extension's export function

Note: BBC's CSS class names may change over time. If columns appear empty, inspect the page source for updated class names.

Running the Analysis

Prerequisites

pip install pandas matplotlib seaborn numpy

Basic Usage

python bbc_analysis_v3.py

The script expects a file named bbc-2025-07-29.csv in the same directory. To use a different filename, modify line 27 in the script.

Output Files

• bbc_disability_coverage_v3.png - Comparative bar chart showing multi-category vs exclusive counts
• bbc_cooccurrence_heatmap_v3.png - Heatmap revealing category intersection patterns
• Console output with detailed statistics and sample uncategorized headlines

Understanding the Methodology

Dual Methodology Explained

This analysis uses two complementary approaches:

1. Multi-category Analysis:

   • Articles can match multiple categories
   • Shows thematic prevalence across the corpus
   • Total will exceed article count due to overlaps
   • Reveals compound framing in journalism

2. Exclusive Category Analysis:

   • Each article assigned to first matching category only
   • Provides true distribution percentages
   • Total equals exactly the number of articles
   • Enables statistical validity

Key Metrics to Track

• Visibility Ratio: Visible disabilities (sensory/physical) vs invisible (mental health/chronic pain)
• Diagonal Dominance: Strong diagonal in co-occurrence matrix = categories presented in isolation
• Compound Framing: Average categories per article (multi-category total ÷ article count)
• Uncategorized Rate: Below 10% indicates comprehensive category coverage

Interpreting Results

Reading the Co-occurrence Heatmap

• Diagonal values: How often a category appears alone
• Off-diagonal values: How often two categories appear together
• Dark red cells: High co-occurrence (categories often linked)
• Light/white cells: Rare or no co-occurrence

A dominant diagonal pattern (as found in BBC coverage) indicates compartmentalized reporting rather than intersectional coverage.

Statistical Significance

• Chi-square test evaluates if distribution differs from random
• p < 0.001 indicates editorial selection patterns
• Compare your results against expected distributions based on disability prevalence

Adapting the Framework

For Different News Sites

1. Update the CSV filename (line 27)
2. Modify column name for headlines:

   headline_col = 'your-headline-column-name'  # line 28

3. Adjust regex patterns for terminology differences
4. Consider regional variations (e.g., "mom" vs "mum")

Adding New Categories

Add patterns to the patterns dictionary (starting line 43):

"Your Category Name": r"(?i)\b(?:keyword1|keyword2|phrase with spaces|abbreviation)\b",

Tips for pattern writing:

• Use (?i) for case-insensitive matching
• Use \b for word boundaries
• Use (?:...) for non-capturing groups
• Test patterns at regex101.com

Time Period Analysis

To analyze specific date ranges, add after line 31:

# Convert timestamp to datetime
df['date'] = pd.to_datetime(df['ssrcss-gfjuy9-Timestamp'], format='mixed')
# Filter for specific period
df = df[(df['date'] >= '2025-01-01') & (df['date'] <= '2025-07-29')]

Troubleshooting

Common Issues and Solutions

High Uncategorized Rate (>15%)

• Review uncategorized headlines for patterns
• Check for BBC-specific terminology
• Consider metaphorical/indirect references
• Add edge cases to relevant categories

Empty CSV Columns

• BBC may have updated their CSS classes
• Inspect page source for new class names
• Update Easy Data Scraper configuration

Memory Error with Large Datasets

• Process in chunks:

  chunk_size = 1000
  for chunk in pd.read_csv('file.csv', chunksize=chunk_size):
      # process chunk

Regex Pattern Conflicts

• Order patterns from most specific to least specific
• Use negative lookahead to exclude false matches:

  r"(?i)\b(?:special school)(?!s? of thought)\b"

Research Applications

Cross-Outlet Comparison

Apply the same categories to multiple news sources:

bbc_results = analyze('bbc_data.csv')
guardian_results = analyze('guardian_data.csv')
compare_outlets(bbc_results, guardian_results)

Temporal Tracking

Monitor changes over time:

results_2024 = analyze('data_2024.csv')
results_2025 = analyze('data_2025.csv')
plot_temporal_changes(results_2024, results_2025)

International Adaptation

• Translate categories for non-English analysis
• Adjust for cultural differences in disability discourse
• Consider local terminology and policy contexts

Performance Optimization

For large datasets (>10,000 articles):

# Use compiled regex for better performance
import re
compiled_patterns = {
    label: re.compile(pattern) 
    for label, pattern in patterns.items()
}

# Use vectorized operations where possible
df['matched'] = df[headline_col].str.contains(
    pattern, regex=True, na=False
).astype(int)

Citation

If you use this methodology in your research:

Academic Citation:

O'Brien, P.C. (2025). The Range of Disability Diversity in BBC News Reporting: 
A Quantitative Content Analysis of the BBC's Dedicated Disability Section. 
GitHub. https://github.com/Eden-Eldith/BBC-Disability-News-Coverage-Analysis

BibTeX:

@misc{obrien2025bbc,
  author = {O'Brien, P.C.},
  title = {BBC Disability News Coverage Analysis},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/Eden-Eldith/BBC-Disability-News-Coverage-Analysis}
}

Support & Consulting

For questions about implementation, custom adaptations, or consultation on diversity monitoring frameworks:

Contact: pcobrien@hotmail.co.uk
ORCID: 0009-0007-3961-1182

I'm available for:

• Adapting this framework to your organization
• Custom analysis of media representation patterns