Overview of the Papers and their Goals:

1. Computationally Defining Local Media
   • Objective: Develop a computational framework to define local media by examining geographic spread patterns of location mentions in news.
   • Methods: Use heatmaps and distance distributions to visualize geographic spread. Compare these patterns to established metrics of audience locality.
   • Example Reference: Northeastern CJ2021 study.
   • Potential Contribution: Provides a foundational method for computationally analyzing local media.
2. Understanding Local News Provision in the UK
   • Objective: Explore how location mentions in news align with socio-economic indices, demographic patterns, and administrative boundaries.
   • Subgoals:
     • Map the spread of location mentions across the UK.
     • Analyze the correlation between news volume and indices of deprivation.
     • Assess geographic proximity of news to local administrative districts (LADs).
   • Potential Models: Predict factors influencing geographically proximate coverage using variables like ownership and newsroom diversity.
   • Potential Contribution: Offers insights into the socio-geographic dynamics of news provision in the UK.
3. Media Ownership and News Provision Patterns
   • Objective: Investigate the role of ownership in shaping geographic patterns of news provision.
   • Subgoals:
     • Compare geographic proximity of coverage between corporate and smaller publishers.
     • Examine patterns of content syndication.
   • Preliminary Work: Content syndication analysis is already completed.
   • Potential Contribution: Highlights the influence of ownership structure on local news dynamics.

Paper 1

Literature Review

Geocoding

• [x] Location extraction
• [ ] Merge back to obtain coordinates
• [ ] Validation: conduct a manual validation of a random subset of geocoded mentions to estimate error rates. This ensures the robustness of the geocoding pipeline.

Analysis

• [ ] Descriptive Statistics:
  • Identify the most frequently mentioned cities and regions in articles.
  • Calculate percentages of mentions by geographic units (e.g., cities, administrative boundaries like LADs).
• [ ] Geographic Spread Metrics:
  • Distance Distributions: Measure the distance between mentioned locations and the outlet's stated coverage area to create distance distributions.
  • Geographic Proximity Metrics: Define "localness" by assessing the proportion of mentions within a predefined radius of the outlet's coverage area.
• [ ] Visualisations:
  • Heatmaps of location mentions to visualize their geographic spread.
  • Cumulative Distribution Plots to compare geographic proximity patterns across outlets (local vs. regional vs. national).

Paper 2

Analysis

• Integration with Demographics: Match geocoded locations with socio-economic data (e.g., indices of deprivation, urban-rural classifications).