Narratologies of Transmedia Networks — Digital Appendix

This repository contains the interactive digital appendix for the doctoral dissertation:

Narratologies of Transmedia Networks
Author: Richárd Fejes
Supervisor: Dr. Gábor Tamás Molnár
Eötvös Loránd University Doctoral School of Literary Studies, 2025

The appendix provides interactive charts, tables, and network visualizations derived from the research dataset. These tools allow users to explore, sort, and analyze narrative and transmedia network data.

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📂 Repository Structure

• index.html
  Main landing page for the appendix. Loads EMH_cleaned_nodes_2025.xlsx and displays it as an interactive searchable table using DataTables.

• chart1.html
  Displays a pie chart of Percentages of Narrative Instance Ownership (Figure 1).

  • Data source: emh_data.json
  • Processing: Groups owners with less than 3% share into an "Other" category.
  • Built with Chart.js.

• parse_xlsx.py
  Python script to:

  1. Load EMH_cleaned_nodes_2025.xlsx
  2. Clean the owner field (trim whitespace, remove empty values)
  3. Export to JSON (emh_data.json)

• emh_data.json
  Processed dataset used by charts for visualization.

• EMH_cleaned_nodes_2025.xlsx
  Primary dataset for EverymanHYBRID node data.

• Other HTML files (e.g., chart2.html, chart3.html, chart4.html, chart5.html, the_beast_map.html, etc.)
  Each corresponds to a separate figure in the appendix, such as:

  • Fluctuations of narrative voices (absolute and percentage)
  • Platform/channel usage
  • Interactive network maps
  • Node/edge tables for EverymanHYBRID and The Beast ARG

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📊 Figures

1. Figure 1: Percentages of Narrative Instance Ownership
   Formula:

   (owner count / total count) × 100%
   

   Minor owners (<3%) grouped into "Other".

2. Figures 2–3: Fluctuation of Dominating Voices (Absolute / Percentages)

3. Figures 4–5: Channel Usage (Absolute / Percentages)

4. Network Maps: Interactive Cytoscape.js-based visualizations with tooltips, filtering, and grouping.

5. Data Tables: Searchable/sortable tables for node and edge data.

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⚙️ Technology Stack

Frontend:

• HTML5, CSS, JavaScript

Libraries:

• Chart.js — Data visualizations
• DataTables — Interactive tables
• Cytoscape.js — Network visualizations
• SheetJS — Excel parsing

Backend/Data Processing:

• Python + Pandas (parse_xlsx.py) for data cleaning and transformation.

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🚀 Usage

1. Local Deployment

Clone the repository:

git clone https://github.com/afejesrichard/elte.transmedia.git

Open any .html file in your browser.

Note: Some visualizations require running from a local HTTP server (e.g., python -m http.server) due to browser fetch restrictions.

2. Data Processing

To regenerate emh_data.json from the Excel file:

pip install pandas openpyxl
python parse_xlsx.py

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📄 License

This repository and its contents are part of the doctoral dissertation Narratologies of Transmedia Networks.
Please contact the author for permissions regarding reuse.