**README**
The csv file in this module represents all the data analyzed to produce the statistics for the paper, "Cultural Values and Cross-cultural Video Consumption on YouTube."
- **country_information.csv** The processed data and aggregated statistics for each country.
Additionally, the attached R script in this module, **CulturalOpenness_Analysis.R**, is provided for replicating the results in the paper.
Finally, the entire video co-consumption raw data, **YouTubeData.tar.gz**, is provided with a Python script for processing the data. The compressed file includes:
- **CountryDataAllAllCategory** A directory contains files named by country codes, which contain video ids listed on each country's daily top 50 over the data collection period.
- **VideoDataAllAllCategory** A directory contains empty files named by video ids in each category collected over the data collection period.
- **Country_Code.csv** Compiled various country codes.
- **generate_coconsumption_network.py** This Python script generates co-consumption networks including networks by individual video categories as well as the combined one mainly used in the paper.
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Variables in **country_information.csv**:
- **country**: List of countries
- **power_distance**: One of Hofstede’s cultural dimensions
- **individualism**: One of Hofstede’s cultural dimensions
- **masculinity**: One of Hofstede’s cultural dimensions
- **uncertainty_avoidance**: One of Hofstede’s cultural dimensions
- **gdp**: Gross Domestic Product (GDP) per capita
- **language_eigenvector_centrality**: Position of a country’s language in the global language networks using its eigenvector centrality
- **internet_users**: Internet penetration rate
- **w_betweenness_0**: Weighted betweenness centrality; alpha = 0.0
- **w_betweenness_0.5**: Weighted betweenness centrality; alpha = 0.5
- **w_betweenness_1**: Weighted betweenness centrality; alpha = 1.0
- **w_closeness_0**: Weighted closeness centrality; alpha = 0.0
- **w_closeness_0.5**: Weighted closeness centrality; alpha = 0.5
- **w_closeness_1**: Weighted closeness centrality; alpha = 1.0
- **composite_openness**: Composite measure of openness using Stirling-Rao diversity
- **migration_degree**: Degree in international migration network
