Digital Religious Authority in Indonesia: A Longitudinal YouTube Search Analysis of Islamic Preachers

Project Overview This repository contains the dataset and automated visualization pipeline for researching the shifting dynamics of digital religious authority in Indonesia. By analyzing YouTube Search interest over a 10-year period, this project tracks the popularity and public engagement of 20 prominent Islamic preachers, ranging from traditional scholars (Kiai/Gus) to teachers (Ustaz) and popular Dai. The goal of this project is to visualize how digital platforms like YouTube have reshaped religious consumption and authority in the world's most populous Muslim-majority nation. Repository Contents Dataset.csv: The primary dataset containing monthly YouTube Search Index values (0–100) for various religious figures. Visualization_Script.ipynb: A Google Colab-compatible Python notebook designed to generate publication-quality figures. README.md: Documentation for the repository (this file). Dataset Specifications Source: Google Trends (YouTube Search Query Results). Region: Indonesia. Timeframe: January 1, 2015 – August 2025. Metric: Search Interest Index (Relative popularity normalized to 100). Categories: All YouTube categories. Technical Methodology To ensure academic rigor and visual clarity, the visualization script implements: Logarithmic Scaling: Used to compare "mega-preachers" with niche scholars effectively without losing data visibility for lower-volume searches. 3-Month Rolling Average: Smoothes out monthly "noise" and sharp spikes to reveal long-term trends ("Looser" lines). Top Figure Filtering: Automatically focuses on the Top 10 figures by total search volume to prevent visual clutter. Zero-Value Correction: Automatically converts index values of 0 to 1 to maintain mathematical compatibility with logarithmic functions. How to Use (Google Colab) Open Google Colab. Create a new notebook and paste the provided Python code. Run the cell; a prompt will appear asking you to upload the CSV file. Select the Google Trends query results...csv file from your local machine. The script will automatically generate and display a high-resolution chart. Dependencies The visualization requires the following Python libraries: pandas matplotlib seaborn numpyDOI: 10.5281/zenodo.19273403