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ARTICLE
Social links vs. language barriers: decoding the
global spread of streaming content
Seoyoung Park1,2,6, Sanghyeok Park3,6, Taekho You4,5 ✉& Jinhyuk Yun1✉
The development of the internet has allowed for the global distribution of content, redefining
media communication and property structures through various streaming platforms. Previous
studies successfully clarified the factors contributing to trends in each streaming service, yet
the similarities and differences between platforms are commonly unexplored; moreover, the
influence of social connections and cultural similarity is usually overlooked. We hereby
examine the social aspects of three significant streaming services–Netflix, Spotify, and
YouTube–with an emphasis on the dissemination of content across countries. Using two-
year-long trending chart datasets, we find that streaming content can be divided into two
types: video-oriented (Netflix) and audio-oriented (Spotify). This characteristic is differ-
entiated by accounting for the significance of social connectedness and linguistic similarity:
audio-oriented content travels via social links, but video-oriented content tends to spread
throughout linguistically akin countries. Interestingly, user-generated contents, YouTube,
exhibits a dual characteristic by integrating both visual and auditory characteristics, indicating
the platform is evolving into unique medium rather than simply residing a midpoint between
video and audio media.
https://doi.org/10.1057/s41599-025-04400-2 OPEN
1School of AI Convergence, Soongsil University, Seoul 06978, Republic of Korea. 2Graduate School of Culture Technology, Korea Advanced Institute of
Science and Technology, Daejeon 34141, Republic of Korea. 3Department of Intelligent Semiconductors, Soongsil University, Seoul 06978, Republic of Korea.
4Institute for Social Data Science, Pohang University of Science and Technology, Pohang 37673, Republic of Korea. 5Center for Digital Humanities &
Computational Social Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
6
These authors contributed equally:
Seoyoung Park, Sanghyeok Park. ✉email: taekho.you@kaist.ac.kr;jinhyuk.yun@ssu.ac.kr
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1234567890():,;
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Introduction
We live in an era of globalization. People now share their
culture in real-time; it is no longer limited to the local
area. For a long time, one has considered that cultural
content mainly spread through the movement of people, while
the increasing migration of these media to the online realm is one
of the most prominent features of the Internet age of the twenty-
first century (Park and Kwon, 2019). Online streaming platforms
enable us to exhibit their content through the internet without
physical (or geographical) barriers; thus, the limitation of direct
human mobility on the spread of content has significantly
diminished. This transition has changed the landscape of content
consumption. For example, the promotion of recorded music has
changed from the conventional purchase of albums and singles in
different physical formats to digital formats via the Internet
(Brown and Knox, 2016), changing the concept of psychological
ownership in music streaming consumption (Sinclair and Tinson
J, 2017). Similarly, the traditional way of consuming movies in
theaters has shifted to the convenience of watching the latest films
anytime, anywhere through over-the-top (OTT) platforms, eras-
ing spatial constraints (Mulla, 2022).
However, the spreading of content is not, nevertheless, affec-
ted only by physical barriers. Consumption and creation of
cultural content are also influenced by historical events and
personal preferences (Michel et al. 2011). The presence of shared
cultural traits and divergent cultural characteristics between
countries can either help or restrain the dissemination of specific
types of content (Baek, 2015). Linguistic affinities between two
groups facilitate the dissemination of information and cultural
exchange (Lazear, 1999). On the other hand, contemporary
information technology provides interactive online platforms,
e.g. social networks and internet messengers, that facilitate the
sharing of knowledge. Hence, the advent of the information
society gives rise to thought-provoking inquiries: do social
interactions impact the spreading of cultural content? If so, how
much more of an impact does social media have than linguistic
barriers? Does this effect remain the same regardless of the
platform or kind of content, or does it vary? However, the fact
that many previous researches have concentrated on particular
platforms and content kinds limits its possibility of addressing
these issues (Baek, 2015; Dueñas and Mandel, 2023; Mulla, 2022;
Sinclair and Tinson J, 2017).
Our study aims to elucidate the complex relationship between
cultural communication, content format, and platform engage-
ment in the context of cultural diffusion. Specifically, we address
two fundamental questions: 1) To what extent do cultural and
linguistic similarities between countries shape the dynamics of
trending content across diverse streaming platforms? 2) How
does the type of content have a role in content dissemination
patterns besides the influence of cultural and linguistic simila-
rities? These inquiries guide our exploration of the complex
interplay between cultural proximity and digital content-
spreading dynamics in the contemporary major media-
consuming landscape, online streaming.
To achieve this, we use three well-known streaming
services–Netflix, Spotify, and YouTube–to try and provide
answers to the above issues. While prior research has made sig-
nificant progress in comprehending cultural dissemination in the
online era, additional data with broader coverage is still required,
ranging from hours-long films to seconds-long short videos. This
dataset selection allows us to examine the spread of culture
among various nations. We then employed two cross-country
connections to examine the diffusion patterns on these platforms:
linguistic similarity between the two countries and social net-
works. Using these datasets, we are able to determine that the
impact of linguistic similarity and social networks on the
dissemination of content differs by platform and data. Linguistic
similarity significantly influences the dissemination of long video
content, as evidenced by the case of Netflix-represented video
media, while Spotify’s music is disseminated more frequently
between two socially interconnected countries, irrespective of
language barriers. Conversely, regarding YouTube, we observe
that it exhibits distinct attributes compared to the aforementioned
platforms-namely, a propensity to consume content generated by
users (as opposed to relying on expert groups for content as in the
case of Netflix and Spotify) and user-generated content as in the
case of YouTube, where users simultaneously serve as providers
and consumers.
Literature review
As internet-based streaming services gradually replaced con-
ventional media services, researchers are beginning to show
interest in these streaming services. Various facets of the
streaming services were studied, which our study is grounded:
media (Gaustad, 2019; Hesmondhalgh, 2021), data science (Cha
et al. 2007; Ibrus et al. 2023; Lotz, 2021; Platt et al. 2015),
business and marketing (Burroughs, 2019; Carroni and Paolini,
2020; Naveed et al. 2017;Vonderau,2019); thus, in this section,
we provide a brief review of the relevant topics and debates
related to the social perspective of streaming platforms that we
focused.
Some studies considered the factors influencing user
engagement and the popularity of content focused on a single
streaming platform (Lewis et al. 2013). For example, how
recommendation algorithms on Netflix shape viewing patterns
were investigated (Gürmeriç, 2019), while the other study
surveyed how user playlists and social curation influence music
discovery on Spotify (Park and Kaneshiro, 2021). Scholars were
also interested in the modeling of the population dynamics of
user-generated content on YouTube (Hoiles et al. 2017).
Another study explored the role of content attributes like genre,
release date, and production value in determining a show’s
success on Korean streaming media (Jang et al. 2021). Similarly,
Park et al. (Park et al. 2019) analyzed how musical character-
istics like tempo and mood influence listener preferences on
Spotify. These studies offer a starting point for comprehending
each internal dynamics of streaming platforms and are crucial
for comprehending how content types affect different audi-
ences, yet lack the consideration of the difference between
various streaming platforms.
The impact of social networks on content consumption has
also been frequently studied. A study shows how user decisions
are influenced by shared viewing experiences and social
recommendations (Bakshy et al. 2012). Similarly, one explored
how social playlists on Spotify contribute to music discovery
and taste formation by neural collaborative filtering (Girsang
et al. 2021). Another study examines the impact of memes and
social media conversations on the virality of content (Berger
and Milkman, 2012). These studies provide important insights
into how social connections affect content popularity and
enable the dissemination of content among various groups, but
they do not take into account the cultural context, including
language barriers. In addition to its increasing prevalence in
some media industries, streaming is also acknowledged as a
developing notion of media convergence (Spilker and
Colbjørnsen, 2020). Consequently, there was a need for cross-
border transmission study.
Many scholars believe that an influential factor for cross-
cultural relationships is cultural similarity and difference (Baek,
2015). Socioeconomic variables such as gross domestic product,
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are possible factors to explain the co-consumption; yet, cultural,
regional, and historical factorsplayaprimaryrole(Dueñasand
Mandel, 2023), while socioeconomic factors downplay the
spread of content. Furthermore, although online social media
enhances global accessibility to cultural products, technological
advancements like internet penetration do not lead to a uni-
versal convergence of cultures (Park et al. 2017). A study sug-
gests that cultural similarities between countries can influence
preferences for specific genres and themes in movies and TV
shows (La Pastina and Straubhaar, 2005). Repeated reports
suggest that linguistic and geographical distance (Bello and
Garcia, 2021;Jangetal.2023; Terroso-Saenz et al. 2023;Way
et al. 2020) affect cross-cultural relationships. These factors and
patterns are observed on diverse platforms or content types
(Liew et al. 2022;TanejaandWebsterJG,2016). Linguistic
barriers may diminish using the translation, and so the effec-
tiveness of subtitling and dubbing strategies in making content
accessible to international audiences was also examined (Borell,
2000). In short, linguistic similarity can act as a barrier or
facilitator for the international spread of video content, yet their
relative importance compared with the social connection,
especially considering the differences among the platforms and
content types, is rarely investigated.
Our work is at the intersection of these pioneers, highlighting
the unique aspect of examining social connections alongside
language barriers and content types for understanding the global
spread of streaming content.
Methods
Collecting online streaming chart data. Our study examines
three leading global streaming platforms: YouTube, Spotify, and
Netflix, chosen for their dominant market positions and extensive
worldwide coverage (Nielsen, 2021; Midia Research, 2023). These
platforms represent distinct content types: video content (Net-
flix), audio content (Spotify), and user-generated videos (You-
Tube). We collected comprehensive ranking datasets from Spotify
and Netflix covering 53 countries, along with YouTube data from
11 countries. This selection allows for a robust cross-platform
comparison of content diffusion patterns across different
media types.
We collected trending charts from three global streaming
services: i) YouTube trending videos, ii) Spotify daily top chart,
and iii) Netflix weekly top chart. We only gathered the data from
10 countries: Brazil, Canada, France, Germany, India, Japan,
Mexico, South Korea, the United Kingdom, and the United
States, which are available in all three services for consistency.
First, we used the YouTube trending video dataset, retrieved
March 20, 2023, from Kaggle (Sharma, 2023), which includes
everyday records of the top 200 trending videos for every country.
Since YouTube API only provides real-time responses from the
requests, we were unable to make past trending video datasets
using API. We also excluded Russia from the analysis because the
overseas video was not listed correctly on Russian YouTube due
to the censorship system. In our target period, from August 12,
2020, to February 28, 2023, there are 1,820,130 records total in
the dataset, which includes 262,721 distinct videos. In addition,
topical categories were collected for each video using the
YouTube Data API (https://developers.google.com/youtube/v3;
elements of topicDetails.topicCategories)on
December 20, 2023; although there also are topical categories in
the Kaggle dataset, we self-collected to enhance the accuracy
because there are no detailed descriptions of the categories in the
Kaggle dataset. Note that there are removed or unlisted videos on
YouTube at the API data collection, and thus YouTube API
responsed only 250,186 distinct videos with 1,747,670 records; for
the categorical analysis, we only used the videos that can retrieve
the category information.
We also collected the Spotify daily top charts between
November 7 and November 8, 2023, from the official website
(https://charts.spotify.com) with Selenium. The dataset contains
daily records of the top 200 tracks for each country, composed of
23,738 unique tracks and 2,002,482 records in total. We limited
the Spotify dataset spanning the same period as the YouTube
dataset: from August 12, 2020, to Februrary 28, 2023, except for
South Korea. As Spotify launched their service in South Korea on
February 1, 2021, we use the daily top chart data only after
February 1, 2021, for South Korea.
For Netflix, we used a list of the top 10 most popular films and
TV shows on Netflix (https://top10.netflix.com) retrieved on
November 8, 2023. The dataset is dated every week from July 4,
2021, to February 28, 2023, including 18,100 TV shows and films
in total. There are 920 unique TV shows and 1,940 unique
movies. Note that Netflix’s charts are weekly charts, and thus the
time resolution differs from the other datasets, yet one can
compare the results because we aim to evaluate the long-term
trends of the 600-days-long datasets rather than daily fluctuation.
Measuring socio-cultural distance between countries
To examine the distance (or similarity) between countries, we
employ additional socio-cultural datasets: i) Facebook social
connectedness index (SCI) (Bailey et al. 2021) and ii) language
lexicon similarity dataset (Bella et al. 2021). (See Supplementary
Fig. S6 for the similarity between the two indices).
SCI data provides a normalized frequency of friendships
between two countries on Facebook, which directly measures
the degree of online social connection between countries. On
the other hand, the language lexicon similarity dataset measures
the similarity between two given languages using lexicon, which
measures the distance (or barrier) between users of two lan-
guages regarding vocabulary. To project the language lexicon
similarity at the country level, we also collected official language
data from CIA World Factbook (Central Intelligence Agency,
2023) because the language lexicon similarity dataset does not
give information about countries’spoken languages. To quan-
tify the linguistic similarity between the two countries, we
calculated the similarity (LLS) between country iand country j
as follows:
LLSij ¼Σs2Li
Σt2LjwsiwtjSðs;tÞ;ð1Þ
where L
i
represents the language set in country i,w
si
represents
the share of language sused in country i,andS(s,t)isthe
lexicon similarity between language sand language tfrom the
language lexicon similarity dataset (Bella et al. 2021).
Note that we did not consider the lexical, grammatical, and
phonological similarity between languages but consider each
language as the unit of calculation (in other words, a one-hot-
vector approach); thus, even though the languages are similar to
some degree, we neglected such innate similarity. As an illus-
trative example, despite being mutually understandable for Hindi
in India and Urdu in Pakistan, these languages are classified
separately in our LLS.
Best fit model distributions of life time-series of contents
To model the event’s lifetime distribution, we commonly use
power law and exponential distributions when it is highly skewed.
For example, the exponential distribution is a suitable model for
the decay of radioactive materials (Istratov and Vyvenko, 1999),
whereas power law decay is an appropriate model for the after-
shocks of earthquakes (Narteau et al. 2005). Such distributions
are characterized by heavy tails, which make it challenging to fita
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suitable distribution from empirical data (Clauset et al. 2009). The
primary motivation for estimating these distributions is that,
while they frequently have a similar visual appearance, under-
standing the precise distribution enables us to predict the
mechanism governing popularity. For example, the power law
indicates that popularity is decided by rewards like positive
associations, whereas the lognormal arises from the process of
repeatedly multiplying separate random distributions
(Mitzenmacher, 2004). Therefore, to find the best-fit model dis-
tribution for the survival time of items in the trending chart, we
choose five models that are frequently used to fit the skewed
distributions and fit the empirical data using the maximum
likelihood estimation as follows (Alstott et al. 2014):
●Power law
pðxÞ¼ðα1Þxα1
min xα
;ð2Þ
●Power law with an exponential cut-off
pðxÞ¼ λ1α
Γð1α;λxminÞxαeλx
;ð3Þ
●Exponential
pðxÞ¼λeλxmin eλx
;ð4Þ
●Stretched exponential
pðxÞ¼βλeλxβ
min xβ1eλxβ
;ð5Þ
●Lognormal
pðxÞ¼ ffiffiffiffiffiffiffiffi
2
πσ2
rerfc lnxmin μ
ffiffiffi
2
pσ
1
´1
xexp ðlnxμÞ2
2σ2
:
ð6Þ
Results
Co-trending contents between countries.Wefirst compared
how many contents are consumed together between the two
countries. Overall, most of trending contents is regional, which is
consumed only in a single country. In Fig. 1, the diagonal cells
display the total number of trending contents for each country,
and for most countries, the number of shared contents is not as
many as the diagonal cells. We find that the United States,
Canada, and the United Kingdom share a large number of con-
tents on YouTube, Spotify, and Netflix TV shows (Fig. 1a, b, and
d, respectively), while a lower number of contents was trended
together in Netflix Films (Fig. 1c). Interestingly, on YouTube,
Canada and France shared 5,821 contents, and the United States
and Mexico shared 3,451 contents, which is a relatively large
fraction of shared contents compared to other countries’pairs.
One possible scenario of this observation is the shared language
user group between two countries. For instance, French is the
official language in Quebec and Spanish is the second most
spoken language in the United States.
When we move our attention to Netflix Films (Fig. 1c), there
is a large block between Western countries. We also observe a
distinguished number of co-trending contents between Mexico
and Brazil. Asian countries present no significant tendency of
co-trending between them. In contrast, the largest block in
Netflix TV shows (Fig. 1d) is composed of Western countries in
addition to India. Using the hierarchical clustering, this largest
block also can be divided into three groups: i) the United States,
Canada, and the United Kingdom, ii) Germany and France, and
iii) India, Brazil, and Mexico (see Supplementary Fig. S1). Japan
and South Korea co-consume a relatively small amount of
contents, although they are geographically nearby; thus, in
contrast to the previous study (Brodersen et al. 2012), there are
(a) (b)
(c) (d)
Fig. 1 Numbers of shared trending contents between countries. a YouTube, bSpotify, cNetflix Films, and dNetflix TV shows. Each point is colored
according to the number of shared contents in a log scale (see the color bar). The labels of the color bar correspond to the quartiles for each platform.
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more influential factors that determine the spreading of
streaming content.
The above results suggest that these possible factors that
promote or inhibit content spreading are i) social connections or
ii) language. Our observation also suggests that this co-trending
tendency is different between streaming services, which provide
different types of content from different producers. While Netflix
mainly serves movies and TV series produced by professional
video producers, Spotify serves musical recordings from profes-
sional musicians, curated by the service providers. On the other
hand, anyone worldwide can share user-generated videos on
YouTube. Therefore, from now on, we aim to determine the more
influential factor between social connectedness and linguistic
similarity in the spreading of streaming content by platforms and
content types.
Dynamics of trending contents by the platforms
To examine the dynamics of global streaming services, we first test
the inertia of trending contents by calculating the total survival
time in the trending chart. We then find the best-fit distribution of
the total survival time distributions for each country by the plat-
forms (see Methods), which are characterized by the size of the
long tails. In the case of YouTube, all countries, except the United
States, follow a stretched exponential distribution. The United
States showed a lognormal distribution. For Spotify, different
distributions were observed by countries. Brazil, Canada, Ger-
many, Mexico, South Korea, the United Kingdom, and the United
States demonstrate a truncated power law distribution; while
France, India, and Japan showed a stretched exponential. Both
Netflix Films and TV shows presented a lognormal distribution
for all ten countries. In short, all three platforms exhibited diverse
dynamics regarding the inertia of trending content (Table 1).
As a next step, we compare the content consumption similarity
between streaming services (Fig. 2). We computed the number of
shared contents between the two countries and compared the
correlation between the two streaming services. A simple linear
regression between the two platforms shows that Spotify and
YouTube exhibit the highest correlation (R2=0.781, Spearman ρ
=0.824). YouTube shows high correlations with all other plat-
forms (Netflix Films : R2=0.687, ρ=0.798 and Netflix TV
shows: R2=0.614, ρ=0.819). Contrary to this, the correlation
between Spotify and Netflix TV shows is relatively low (R2=
0.318, ρ=0.601 (Fig. 2e)). One may consider the fact that Spotify
serves audio-oriented content, whereas Netflix serves mainly
video-oriented content. Human perceptions of audio-oriented
and video-oriented content are different; so cultural proximity
may have different influences on disseminating the content. It
partially explains the low correlation between Spotify and Netflix
TV shows. Going a step further, YouTube’s strong relationships
with other streaming services could be attributed to the platform’s
availability of audio-video hybrid content.
Social links vs. language barriers
We extend the study to answer which socio-cultural factors play a
more important role in trending content spreading. First, we
apply the social connection as a proxy of social similarity
(Abisheva et al. 2014). We used the Social Connectedness Index
(SCI) provided by Meta, which describes the strength of the social
connection between countries. When the SCI is high, the users in
the two countries tend to be in a friendship. First, YouTube and
Spotify show high correlations between SCI and the number of
co-trending content (Fig. 3a: R2=0.480, ρ=0.665 and (b): R2=
0.423, ρ=619), whereas correlations between SCI and Netflix
(both Films and TV shows) are less significant (Fig. 3c: R2=
0.113, ρ=0.271 and (d) : R2=0.088, ρ=0.311). In short, when
the two countries are socially connected, they tend to share the
same audio-oriented trending content more.
While the social tie is correlated with the spreading of audio-
oriented content, we also find that linguistic similarity influences
more to the spreading of video-oriented content. YouTube,
Netflix Films, and Netflix TV shows are highly correlated with
language lexicon similarity (R2=0.697, 0.561, and 0.482 in
Fig. 3e, g, and h, respectively). One possibility is that to fully
understand video-oriented content, such as films and TV shows,
one requires the ability to understand nuanced expressions so
that users in the same language can be more familiar with the
(a)
(d)
(b) (c)
(e) (f)
Fig. 2 Cross-platform comparison for the numbers of co-trending contents between countries. The orange solid line represents a linear regression line
between two platforms measured in a log-log scale (y~xk), where we also measure the coefficient of determination (R2) and Spearman rank correlation
(ρ). a–cWhile YouTube shows a high R2( > 0.6) with every other platform, d,eSpotify and Netflix have a relatively lower coefficient of determination
between them. fIt’s interesting to note that the relationship between Netflix’s Films and the TV shows has a lower R2than their relationship with YouTube.
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content. However, because of the lack the user information, we
left it for further study. Contrary to this, Spotify exhibits a lower
correlation than others (R2=0.288, ρ=0.428, Fig. 3f). This
result implies that the spreading of audio-oriented content, such
as music, is influenced less by the language than that of video-
oriented content.
Combining the results, we can summarize that the influential
factor of content spreading depends on the type of content. The
spreading of auditory content is highly correlated with social
connection, whereas the spreading of visual content is limited by
language. One interesting point is that YouTube shows a dual
characteristic of audio-based and video-based content. Indeed,
YouTube has a various type of content uploaded by individuals.
In short, YouTube’s high correlation with both social connections
and linguistic similarity may come from YouTube’s wide range of
content types; however, it also can be due to the unique char-
acteristics of YouTube. Netflix and Spotify are a sort of alternative
service to legacy media. Netflix serves as an alternative for thea-
ters and Spotify serves as a substitute for music media such as
compact discs. However, YouTube does not have such a coun-
terpart. Therefore it necessitates a more in-depth analysis of two
possible reasons.
Decomposing YouTube into the topical categories
We step into analyzing categories in YouTube content to figure
out the underlying reason behind the high correlation of You-
Tube for both social connections and linguistic similarity. To do
this, we assign the categories for each content using YouTube
Data API (See Methods). Figure 4a, b display R2and Spearman
rank correlation of the number of co-trending videos with SCI
and language lexicon similarity between countries, by the cate-
gory. We found that the correlation varies by the categories. For
instance, contents in the musical category have a high correlation
with language lexicon similarity (green circles in Fig. 4a, b), while
contents in the sports category have a large variance with both
language lexicon similarity and SCI (blue circles).
In the musics category group, YouTube contents show a
higher correlation with language similarity than the SCI,
Table 1 The best-fit distributions and their log-likelihoods for the total survival time distribution in the trending charts for each
country (see Methods).
YouTube Spotify Netflix TV Netflix Films
BR SE −63804.46 TP −13952.09 LN −976.04 LN −1506.11
CA SE −65111.92 TP −16828.35 LN −1172.66 LN −1486.51
DE SE −73416.77 TP −23780.35 LN −1070.51 LN −1377.75
FR SE −69299.91 SE −19838.77 LN −1090.58 LN −1444.85
GB SE −64322.76 TP −16224.22 LN −1145.87 LN −1457.60
IN SE −101575.88 SE −9858.39 LN −857.64 LN −1251.91
JP SE −52606.48 SE −9806.48 LN −760.12 LN −1386.82
KR SE −54897.41 TP −13807.96 LN −697.07 LN −1325.81
MX SE −55512.13 TP −10346.51 LN −967.65 LN −1516.86
US LN −67029.40 TP −18026.60 LN −1093.63 LN −1394.56
We have abbreviated Stretched exponential as SE, Truncated Powerlaw as TP, and Lognormal as LN in the table. The full fitting results of all five models can be found in Supplementary Table S1.
(a)
(c) (d) (
g
) (h)
(f)(b) (e)
Fig. 3 The correlation between the number of shared trending contents and two proxies of social similarity. a–dFacebook Social Connected Index
(Bailey et al. 2018) and (e–h) language similarity (see Methods). The orange solid line represents a linear regression line between two platforms measured
in a log-log scale (y~xk), where we also measure the coefficient of determination (R2). Spotify shows a comparatively stronger R2for social networks
(Facebook SCI) than linguistic similarity (compare (b) with (f)), yet linguistic similarity displayed a greater R2for Netflix (compare (c,d) with (g,h).
YouTube shows high R2for both proxies of social similarity (aand e).
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where the median R2is 0.748 and 0.369 for language lexicon
similarity and SCI, respectively. Recall the observation that
Spotify shows a higher correlation with the SCI than language
lexicon similarity (Fig. 3), YouTube’s musics category groups
displays different characteristics to Spotify. If one looks in
detail, one may find that Reggae,Jazz,andClassical
music show low R2with language lexicon similarity, which is
consistent with Spotify’s results, yet many other musical
categories show a high correlation with language lexicon
similarity.
Fig. 4 Interrelationship between the correlation of the trending videos on YouTube with the Facebook SCI and the language similarities across
categories. For (a), the x-axis represents the R2of the number of shared trending videos to the Facebook SCI between countries, whereas the y-axis
represents the R2between the number of shared trending videos to the language similarity. Panel (b) shows similar relations using the Spearman rank
correlation instead of R2. For panels (a)and(b), the color of circles represents the category groups: Musics categories (green), Games categories (purple),
Sports categories (blue), Visual arts (red), and others (grey); See Supplmentary Table S5 for full list of categories and Supplmentary Tables S6 and S7 for the
detailed statistics. The diameter of the circles corresponds to the number of country pairs that have mutually shared trending videos. The dashed linein(a)
and (b) represents a median value of language lexicon similarity and SCI of all categories in the data. Panels (c–f) display the scatter of the number of shared
trending videos with Facebook SCI (cand e) and language similarity (dand f), respectively, for two example categories: (c,d)Music and (e,f)Films.
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We also find that most of the games category groups exhibit a
high correlation with the language lexicon similarity (median R2
=0.716). Since the gaming contents are visual-centered with the
narrative and textual elements within games (such as dialogue
and storylines), linguistic factors play a crucial role in their global
dissemination. There is no noticeable influence of social con-
nections or linguistic similarity for the sports category, although
our dataset includes important sports events such as the Tokyo
2020 Summer Olympics,FIFA World Cup Qatar 2022, and Beijing
2022 Winter Olympics. One potential cause is that each country
has distinct broadcasting companies that own regionally exclusive
broadcasting rights; there can be identical events yet from dif-
ferent sources.
One interesting point is that the correlation with the linguistic
similarity of the visual arts category group (median R2=0.502) is
lower than that of the musics category group (median R2=
0.748), although it shows similar correlations with Netflix Films
and TV shows (R2=0.561 and 0.482, respectively; see Fig. 3). In
addition, the correlation with the SCI of the visual arts category
group (median R2=0.413) is high, compared to Netflix Films (R2
=0.113, ρ=0.271) and TV shows (R2=0.088, ρ=0.311).
Indeed, we can see the clear correlations in Fig. 4c–f compared to
Fig. 3b, c and f, g. These findings also support our former finding
that YouTube, as a completely new type of media, shows different
characteristics compared with the alternatives of legacy media;
YouTube shows a dual characteristic of visual-oriented and
audio-oriented content.
Discussion
What streaming platforms spread is not only in the media con-
tents but also the cultures, which facilitate communication
between communities with different habitus in contemporary
society. Previous studies mainly focused on individual platforms
or within specific countries (Pinto et al. 2013). Although there are
attempts to analyze the global perspective (Abisheva et al. 2014), it
mainly considers the impact of geographical barriers (Brodersen
et al. 2012), which is now gradually diminished (Yoon et al. 2023).
Our study takes a wider angle of view by analyzing co-trending
content between ten countries in the three most popular global
streaming services: YouTube, Spotify, and Netflix. We then try to
elucidate the underlying influential factors of spreading content by
using social connection and linguistic affinity.
Our findings suggest that intercountry content spreading pat-
terns are different by streaming services. To elucidate the
underline factors influencing the spreading, we employed two
proxies of social similarity, SCI representing the tendency of
direct friendship between countries’populations and lexicon
similarity accounting for the similarity and barrier due to the
languages (Yoon et al. 2023). The spreading of music (or auditory
content), evidenced by Spotify, is largely influenced by social
connectivity and insignificantly influenced by linguistic barriers.
Dissemination of video content, observed from Netflix, depends
more on language rather than social connectivity. Language and
social connectivity both show a large influence on content
spreading on YouTube. One may suppose that this is because
YouTube contains both auditory and visual content, yet our study
shows that the platform’s strong correlation is not due to this
because language and social connectivity have a significant impact
on the spreading of both music-focused and visual-focused
categories on YouTube. Instead, due to the unique prosumer
behavior in YouTube, which one being both producer and con-
sumer simultaneously (Holland, 2016), users may be more tightly
engaged in social connection, while the language similarity
facilitates the spreading of the contents. Therefore, YouTube
establishes a unique ecosystem, rather than a mixture of legacy
ecosystem of music and video content separating the consumers
and producers.
We believe that such data have considerable potential for
future research also. In this study, we use trending content in
streaming platforms, which covers a relatively small number of
content concerning the entire volume of content in the platforms;
in addition, although we selected three well-known, and global,
streaming platforms there are thousands of other streaming
platforms, and thus we hardly cover entire user pool. Our find-
ings are possibly due to the limited user pools. For instance,
because Spotify is not a major service in South Korea, the user
pool in South Korea is biased toward heavy listeners preferring
Western music. Additionally, the differences can be from the
business model, that is paid-subscription model for Netflix
compared with the advertising-subscription hybrid model of
YouTube and Spotify. Note that Spotify in South Korea only
offers a paid-subscription model due to market regulation, which
may induce different user behaviors. Because we only use the
degree of friendship as the proxy of social connection, the
implication of the study will enhance with the additional analysis
on the actual spreading behavior (Berger and Milkman, 2012;
Weng et al. 2013), along with the detailed analysis on the socio-
cultural background of the group of users based on their platform
selection and living country. For future study, therefore, it is
worthwhile to incorporate a more contextual approach to lin-
guistic similarity that considers the cultural, lexical, grammatical,
and phonological relationships between languages. Such an
approach would more accurately reflect the potential for content
sharing and cultural exchange between regions with linguistically
similar but officially distinct languages, e.g., Hindi and Urdu.
We demonstrate how language usage and social connections
affect the spreading of online content, suggesting that individuals
may react differently to the same content based on their back-
grounds. Thus, quantifying the differences in interest changes
based on their social background and language may be beneficial
to understanding the hidden pattern of human behavior. By
spotlighting the influential factors of cultural spreading, we want
to shed light on the unexplored mechanism underlying the gen-
eral rules of cultural spreading and adoption. From these findings,
we extended theories that were validated within a single cultural
area or a single media platform, contributing to a broader com-
prehension of cultural dissemination. In addition, our findings
suggest that user-generated content should be differentiated from
music or video content. It will help to analyze trends on other
user-generated social platforms such as TikTok Furthermore,
these insights provide practical implications for the streaming
platforms. They might consider format characteristics and
recommendation algorithms to better cater to a global audience,
fostering cross-cultural exchange and expanding their user base.
Finally, we emphasize that our research has potential wider
implications in contemporary society, not restricted to streaming
platforms, as soft power is increasingly important in con-
temporary society (Nye, 1990).
Data availability
All of the datasets used in the current study are publicly acces-
sible. YouTube data is collected from Kaggle (https://doi.org/10.
34740/KAGGLE/DSV/7530407), and YouTube Data API (https://
developers.google.com/youtube/v3) is used for additional data
collection. The Spotify daily top charts are available on their
official website (https://charts.spotify.com), so as Netflix weekly
top charts https://top10.netflix.com). Aggregated dataset includ-
ing socio-cultural measures and code used for analysing during
the study are available on Github (https://github.com/seoyypark/
Social_links-Language_barriers). For additional research or
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8HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:76 | https://doi.org/10.1057/s41599-025-04400-2
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
citation including the listed datasets, it is recommended to follow
the terms and conditions specified by each dataset provider. For
more detailed descriptions of the collection period and methods
for each platforms, please check section Collection online
streaming chart data and Measuring socio-cultural distance
between countries.
Received: 11 July 2024; Accepted: 14 January 2025;
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Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) funded by
the Korean government (grant No. NRF-2022R1C1C2004277 (T.Y.) and
2022R1A2C1091324 (J.Y.)). This research was also supported by the Global Humanities
and Social Sciences Convergence Research Program through the National Research
Foundation of Korea(NRF), funded by the Ministry of Education
(2024S1A5C3A02042671 (J.Y.)). The Korea Institute of Science and Technology Infor-
mation (KISTI) also supported this research by providing KREONET, a high-speed
Internet connection. This work was also supported by Innovative Human Resource
Development for Local Intellectualization program through the Institute of Information &
Communications Technology Planning & Evaluation(IITP) grant funded by the Korea
government(MSIT) (IITP-2024-RS-2022-00156360). The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Author contributions
All authors conceived and designed the analysis, collected the data, contributed data or
analysis tools, wrote the paper. S.P. and S.P. performed the analysis.
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants performed by any of
the authors.
Informed consent
This article does not contain any studies with human participants performed by any of
the authors.
Additional information
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available at https://doi.org/10.1057/s41599-025-04400-2.
Correspondence and requests for materials should be addressed to Taekho You or
Jinhyuk Yun.
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