Global Streams, Local Currents: A Data Analysis on Global VOD Content Consumption

TL;DR

The paper investigates global VOD consumption patterns using a network approach built from Netflix Top-10 data across 71 countries over 822 days. It converts rankings into country-specific score vectors, computes daily and overall NSC distances between country pairs, and builds a weighted network in which similarity is reflected by edge weights; the Louvain algorithm is then used to identify three robust country groups. The results reveal three geo-linguistic clusters—North America and Pan-Europe, Asia and Middle East, and Central and South America—with Turkiye often occupying boundary positions, and show that major producers like the United States, South Korea, and Colombia exert distinct influences on these groupings. A notable post-2021 rise in South Korean content (e.g., Squid Game) correlates with shifts in cross-regional consumption, suggesting that global hits can reshape long-standing cultural patterns in VOD markets. Overall, the study demonstrates that cultural histories continue to steer global streaming behavior despite rapid platform and content-production changes, and it offers a robust methodological framework for tracking temporal and cross-cultural shifts in media consumption.

Abstract

This study explores global video on demand content consumption patterns through a network-based approach. We used Netflix's 'TV-shows' ranking data, spanning 822 days across 71 countries, to construct a network where countries are represented as nodes and consumption similarities are reflected as link weights. By applying the Louvain algorithm, we identified three distinct consumption groups, 'North America and Pan-Europe', 'Asia and Middle East', and 'Central and South America group'. These groups align closely with geographic, historical, and linguistic divisions, despite no predefined grouping criteria. Notably, Turkiye, often considered a cultural and regional crossroads, exhibited some classification ambiguity but was ultimately grouped with Asia and Middle East. Our findings also show that the United States accounts for the largest share of content consumption across all groups, while South Korean content, particularly after the success of "Squid Game" in 2021, has gained and maintained popularity in Asia, the Middle East, and Latin America. This study, based on data, demonstrates that deep-seated cultural histories continue to shape global consumption patterns, even amidst rapid changes in media platforms and content production dynamics.

Figures (6)

• Figure 1: VOD Similar Consumption Groups. The North America and Pan-Europe group (n = 35) is color-coded in red, the Asia and Middle East group (n = 18) is color-coded in green, and the Central and South America group (n = 18) is color-coded in blue.
• Figure 2: Top 5 content producing countries and their scores within each group. The background color of the graph represents the group being averaged, and the color of the hatched bars represents the group that the producing country belongs to. ‘US’ stands for the United States, ‘KR’ for South Korea, ‘CO’ for Colombia, ‘UK’ for the United Kingdom, ‘ES’ for Spain, ‘FR’ for France, ‘JP’ for Japan, ‘TR’ for Turkiye, and ‘MX’ for Mexico.
• Figure 3: Similar consumption groups by the leading content-producing countries. (a)Similar consumption groups by US-produced content, (b)Similar consumption groups by South Korea-produced content, (c)Similar consumption groups by Colombia-produced content
• Figure 4: Trends in the rankings of US, South Korean, and Colombian content by similar consumption groups. The red triangle represents the scores for American content, the green circle represents South Korean content, and the blue star represents Colombian content. (a)The Asia and Middle East Group, (b)The North America and Pan-Europe Group, (c)The Central and South America Group.
• Figure A1: (a) Total scores $s_{_{\!T}}^c$ of $N_\text{cont} = 1769$ Netflix titles arranged by rank, plotted on a log--log scale. (b) Distribution of total scores $s_{_{\!T}}^c$ on a log--log scale using bins of width 10. The vertical axis indicates the number of contents $n(s_{_{\!T}}^c)$ falling within each bin.