Newspaper × Theme Heatmap

A comparative view of how different newspapers emphasise themes around mining, environment, labour, and community resistance.

Comparative press analysis Thematic clustering Environmental memory

1. What this heatmap shows

Each row is a newspaper. Each column is a theme (for example: Environmental Memory, Labour & Disability, Community Resistance).

The colour intensity in each cell represents how many articles in that newspaper are tagged with that theme (or a normalised score). Darker cells mean stronger emphasis or more frequent coverage.

2. How to use this as a researcher

Identify “loud” newspapers: Which titles have the darkest rows for environmental harm or mine accidents? These are key sites of environmental and labour advocacy, or at least visibility.
Track editorial bias: Compare, for instance, whether one newspaper downplays Labour & Disability while foregrounding Governance & Policy. This can speak to class positions, regional politics, or ownership structures.
Locate silences: Empty or pale cells may signal sites of archival silence, not an absence of suffering. They invite further qualitative investigation.
Build case studies: Pick one newspaper with a distinctive pattern (for example, high Community Resistance) and return to specific articles for close reading and discourse analysis.

3. Step-by-step interaction guide

Hover over any cell to see the newspaper name, theme, and exact count.
Use the zoom tools (magnifying glass icons) in the Plotly toolbar if you have many newspapers or themes and want to zoom into one part of the matrix.
Download the visual as a PNG using the camera icon in the toolbar for use in slides, papers, or reports.
Re-read peaks: After spotting a dark cell (for example, “Punch × Accidents & Safety”), open MMM_DATA.html, filter by that newspaper and theme, and then dive into the individual articles.

4. Linking back to MMM data and NVivo-style coding

          Workflow suggestion: Use this heatmap as a map of attention.
          First, identify newspapers and themes with high values. Second, export or filter those
          articles in the Data Table page. Finally, bring the resulting subset into
          NVivo or your coding environment to explore discourse, metaphors, and silences in more detail.
        

5. Data & method (short note)

The underlying matrix aggregates article counts by newspaper and theme. Themes are generated from your mining corpus using a combination of keyword dictionaries, topic modelling, and manual refinement around core concerns such as environmental memory, eco-technological disability, and community contestation.

6. How to cite this visualisation

Suggested citation (example):
“Media Mining Memory: Newspaper × Theme Heatmap,” Reports on Mining in Nigeria (digital project), accessed [date], GitHub Pages / project URL.

Interactive newspaper–theme heatmap

Compare how newspapers differ in their coverage of key mining themes. Hover for counts, zoom into dense areas, and export for teaching or publication.