# The Web unpacked: a quantitative analysis of global Web usage

Henrique S. Xavier<EMAIL_ADDRESS>0000-0002-9601-601X NIC.brAv. das Nações
Unidas, 11541, 7º andarSão PauloSPBrazil04578-000

(2024; 19 April 2024)

###### Abstract.

This paper presents a comprehensive analysis of global web usage patterns
based on data from SimilarWeb, a leading source for estimating web traffic.
Leveraging a dataset comprising over 250,000 websites, we estimate the total
web traffic and investigate its distribution among domains and industry
sectors. We detail the characteristics of the top 116 domains, which comprise
an estimated one-third of all web traffic. Our analysis scrutinizes various
attributes of these domains, including their content sources and types, access
requirements, offline presence, and ownership features. Our analysis reveals a
significant concentration of web traffic, with a diminutive number of top
websites capturing the majority of visits. Search engines, news and media,
social networks, streaming, and adult content emerge as primary attractors of
web traffic, which is also highly concentrated on platforms and USA-owned
websites. Much of the traffic goes to for-profit but mostly free-of-charge
websites, highlighting the dominance of business models not based on paywalls.

world wide web, traffic share, website visits, web browsing, web usage,
platforms

††conference: 4th International Workshop on Computational Methods for Online
Discourse Analysis; May 13–17, 2024; Singapore††copyright:
acmcopyright††journalyear: 2024††doi: XXXXXXX.XXXXXXX††price: 00.00††isbn:
978-1-4503-9419-2/24/05††ccs: Information systems Web applications††ccs:
Information systems Traffic analysis††ccs: Applied computing Law, social and
behavioral sciences

##  1\. Introduction

The World Wide Web (also called the Web) is an online decentralized and owner-
free information system released to the public domain in 1993 (Berners-Lee,
1990; CERN, 1993). It is an application of the Internet protocol suite, a
computer communication protocol that is also decentralized and owner-free. The
Web itself has no moderation or gatekeepers and, for a long time, had almost
no governmental regulation. It was designed to be highly system-independent,
technically simple, easy to implement, and permissive to format and content –
all features associated with the Web’s massive success and adoption worldwide
(Gandon and Hall, 2022). Currently, the Web is the best-known and most
pervasive Internet application. Web browsing is a dominant computer activity
(Crichton et al., 2021), and Web-based companies figure among the largest and
most valuable companies in the World (Evans and Gawer, 2016). Moreover, HTML,
CSS, JavaScript, and WebAssembly technologies have promoted the Web as a
universal platform for building computer applications (Bouras et al., 2015;
Taivalsaari and Mikkonen, 2011).

The importance of the Web, its lack of explicit management, and its vast
potential have raised questions on what are its realized, actual
characteristics in terms of main uses, applications, and content types, how
visits are distributed among websites, who are the content publishers, how is
the content typically produced, and much more. In the past, the Web has been
thoroughly analyzed in terms of its content and graph properties (e.g., the
number of webpages covering a given topic (Chakrabarti et al., 2002; Huizingh,
2000) and the number of hyperlinks connecting them (Adamic and Huberman,
2000b; Huberman, 2001)). However, previous accounts about the characteristics
of the Web have primarily been based on metrics other than usage (e.g., link
structure, number of websites, or companies’ market value) or were built on
anecdotal accounts and non-quantitative data. Some exceptions are: (Adamic and
Huberman, 2000a; Webster and Lin, 2002) that showed for a restricted subset of
users and websites that visitors are distributed among websites in a power-
law-like fashion; and (Agarwal and Sastry, 2022), that used Alexa’s ranking
list to select the 100 most visited domains, Google trends to estimate their
popularity and Fortiguard’s classification to verify the popularity of
different topics, among other things.

This paper aims to provide a data-driven general picture of web usage. Based
on data about the number of monthly visits to web domains, we aim to estimate:

  1. (1)

the number of domains (and which ones) required to form a representative
picture of global web usage;

  2. (2)

the importance of topics explored on the Web; and

  3. (3)

the prevalence of broad website characteristics (e.g., access barriers,
content sources, and ownership) typically encountered in web visits.

This paper is organized as follows: Sec. 2 presents the characteristics of our
data and our cleaning process. In Sec. 3.1, we analyze the temporal variation
of monthly visits to estimate the stability of our findings. We describe the
distribution of visits in Sec. 3, where we also estimate the total monthly
visits of the whole Web. We distribute the visits among industries in Sec.
3.3. Sec. 3.4 delves into a detailed analysis of the 116 most visited domains,
exploring ownership, content production, login requirements, and the provision
of applications. Finally, we summarize our analysis and present our
conclusions in Sec. 4.

##  2\. Data Set

The decentralized nature of the Web complicates our ability to understand its
characteristics. Without a centralized index and unified access log, accurate
measurements of web traffic (such as monthly website visits) and links become
challenging (Scheitle et al., 2018; Jansen et al., 2022). Regarding web
traffic measurement, there are three basic methods:

  1. (1)

User-centric: This method involves using software (e.g., browsers, extensions,
or other applications) installed on the user’s device to monitor that user’s
behavior.

  2. (2)

Server-centric: Here, JavaScript and HTTP request logs are employed to monitor
accesses and behavior on a given website.

  3. (3)

Network-centric: This method involves an Internet Service Provider (ISP)
monitoring HTTP requests from its customers.

SimilarWeb, a company that combines all three methods, reportedly tracks a
representative panel of users to ensure that all domains visited a minimum
number of times are monitored (SimilarWeb, 2024b).

The data analyzed in this paper are from SimilarWeb’s Starter Plan, covering
August to October 2023. This plan provides data about the most visited domains
in each of the 210 industry segments specified by SimilarWeb, limited to
10,000 domains per industry. During the covered period, data was available for
1,336,963 domains. For each domain, SimilarWeb estimates the average monthly
visits, month-over-month visit variation, average number of unique visitors,
average visit duration, and fraction of visits from mobile and desktop. Due to
limited accuracy, monthly visits and unique visitors lower than 5,000 figure
as “$<5,000$” (SimilarWeb, 2024b). For some low-ranking domains, these data
might show as “-”. In general, subdomains are not distinguished from their
parent domains (e.g., visits to docs.google.com are accounted for as part of
visits to google.com along with visits to other Google subdomains), and
transitions from one subdomain to another do not count as an extra domain
visit, just as a single one.

The quality of SimilarWeb data has been evaluated in previous academic works
and is considered good enough for relative ballpark measurements between
domains. Jansen _et al._ used Google Analytics for 86 websites as a truth
table and identified a systematic bias of 20% in SimilarWeb’s monthly visits
estimates (Jansen et al., 2022). Prantl compared monthly visits from
SimilarWeb and NetMonitor111https://www.spir.cz/projekty/netmonitor/ for 485
Czech websites and found an average absolute difference of about 42% between
these two sets of measurements (Prantl and Prantl, 2018). Enterprise reports
have indicated similar scenarios (Hardwick, 2018; Diachuk et al., 2021).
However, given that our data on monthly visits cover over seven orders of
magnitude (from 5 thousand to 86 billion), the reported deviations do not
significantly affect the overall distribution of visits among websites. The
data still provides precise ranks, with a Pearson correlation of 95% between
monthly visit estimates from SimilarWeb and Google Analytics (Jansen et al.,
2022) and a Spearman correlation of 96% between SimilarWeb’s and NetMonitor’s
ranks (Prantl and Prantl, 2018).

In our analysis, we excluded domains with values of “$<5,000$” and “-” for
average monthly visits. For each industry, we assumed that SimilarWeb is
complete up to a certain threshold (the domain with the lowest average monthly
visits). In other words, we assumed that domains missing from an industry’s
list must have average monthly visits lower than the least visited domain in
the list. However, it is essential to note that being complete in a given
industry sector does not imply the entire dataset is complete, as SimilarWeb’s
Starter Plan limits the number of domains per industry to 10,000. To ensure
completeness in the entire dataset, we must enforce a threshold on average
monthly visits of 140,484, the largest individual industry threshold. This
complete dataset, considered in our analysis, contains 254,661 domains and
will be ranked by average monthly visits.

##  3\. Analysis

###  3.1. Month-over-month variation

SimilarWeb provides information on how much the monthly visits of a domain
increased or decreased in the last month compared to the previous one. This
change, represented as a percentage, is called month-over-month variation
(MoM). Due to practical data access issues, we could only analyze the MoM
distribution of the 8,000 most visited domains. Fig. 1 shows that, despite a
moderate variance (68% of the MoMs stay between -5% and 19%), outliers are
frequent both at low and high MoMs, strongly affecting non-robust statistical
measures like the mean and the standard deviation. The average absolute
difference between consecutive monthly visits is 15%, about three times
smaller than the average difference between SimilarWeb and NetMonitor
measurements (Prantl and Prantl, 2018). This slight variation indicates that
methodological differences in traffic measurement are more important sources
of error than differences in the time frame.

We also see that the distribution favors positive MoMs – the distribution
median is 3,6%, and bootstrapping shows this value is significantly greater
than zero. We suggest three explaining factors for this increase in monthly
visits over time:

  1. (1)

the total traffic on the Web is increasing, with people spending more time or
more people gaining access to it (Union, 2023);

  2. (2)

the top ranking websites are concentrating traffic, at the expense of less
visited ones (Society, 2019); and

  3. (3)

there is a seasonal pattern (similar to the one in (Liu et al., 2018)) in
which traffic typically increases from September to October.

Further quantitative exploration of these factors would require data
distributed over a larger time frame.

Figure 1. Histogram of MoM for the 8,000 most visited websites. The values
were clipped at 200% to improve plot readability.

The distribution is heavy-tailed, with a peak slightly above 0. It ranges from
-100% to 200%.

Fig. 2 shows the MoM distribution as a function of rank position. The dark
blue line represents the median computed in a moving window containing 200
domains, and the gray line represents the median absolute deviation (MAD) –
multiplied by $-1$ to avoid overlapping with the blue line – computed in the
same window. Apart from a statistically significant decrease in the MAD at the
top of the rank (meaning that the monthly visits variation is more predictable
for the top 2,000 websites), the distribution is reasonably stable in the
remaining range.

Figure 2. MoM vs. rank position. The dots represent individual domains, and
lines represent moving statistical measures. Outliers beyond $\pm 40\%$ are
not shown.

The dots are homogeneously scattered in the horizontal direction (rank
position), with a vertical concentration around a MoM of 3.6%. The lines
representing the median and MAD are noisy but follow a mostly constant trend,
except for the MAD at the top of the rank.

Since the MoM measures fluctuations in a domain’s visits and its distribution
is stable for most of the rank, we adopted a centered (i.e., median-
subtracted) version of the MoM distribution shown in Fig. 1 as a model for the
statistical error on the monthly visits estimates, assuming the errors are
independent and identically distributed. We will use this error model in the
following section.

###  3.2. Traffic distribution

We identified that the traffic (i.e., monthly visits) is distributed among
domains in a power-law-like fashion. Previous papers have reported similar
findings regarding the number of connections between internet nodes (Faloutsos
et al., 1999), number of incoming links to a website (Adamic and Huberman,
2000b), the time spent by users on websites (Crichton et al., 2021), and
number of unique visitors to domains (Adamic and Huberman, 2000a; Webster and
Lin, 2002). Despite being correlated, these quantities are not the same. Among
these studies, (Adamic and Huberman, 2000a) had the most similar metric and
data to ours: it used network-centric data from 60,000 AOL users active in
December 1997 to rank 120,000 domains concerning the number of unique
visitors.

Fig. 3 shows a log-log plot of the monthly visits $V$ vs. rank position $p$
for the domains in our dataset. A power-law $V(p)=V_{0}p^{\beta}$ appears as a
straight line in such a plot. We obtain a best-fit model to our data with
$V_{0}=4.1\times 10^{11}$ and $\beta=-1.19$. We also represent as pink bands
the range covered by a $2\sigma$-equivalent interval (i.e., from the 2nd to
the 98th percentile) assuming statistical fluctuations described in Sec. 3.1.
The narrowness of the bands demonstrates that the properties of the traffic
distribution should be reasonably stable over time (at least for a few months)
and that the overall ranking of the domains is not significantly affected by
measurement errors.

Figure 3. Average monthly visits as a function of the domain’s position in the
rank. Light bands represent a $2\sigma$ variation in the visits from month to
month.

The plot employs log-log scales, and the average monthly visits vs. rank
position forms an almost straight line. The band representing month-to-month
variations is thin compared to its extension.

To calculate the share of the total web traffic captured by each domain in our
dataset, we first need an estimate of this total traffic. For that, we
extrapolated the best-fit power law to position 354 million, the number of
registered domains as of June 2023 (Verisign, 2023), and aggregated the
measured traffic along with the estimated traffic beyond position 254,661,
resulting in a total of 781 billion visits per month. With 5.35 billion
internet users as of January 2024 (Petrosyan, 2024), this amounts to 4.9
domain visits per person per day, a reasonable ballpark figure: previous work
has shown that users from a metropolitan area in the United States visited an
average of 20.1 websites per day on their computers, while the least active
user (out of 257) visited an average of 2.9 (Crichton et al., 2021). Including
rural regions and other countries should lead to a lower average. Fig. 4 shows
the estimated traffic share for the 254,661 domains in our data.

Figure 4. Best estimate of the cumulative traffic share of domains as a
function of position in the rank (red line). The violet band represents the
systematic uncertainty.

The best-fit trend grows continuously and passes 50% at position 2,938,
reaching 81% at the last position. The width of the systematic uncertainty
band grows with position. At the right end, it goes from 57% of the total
traffic share to 90%.

Extrapolations are risky as there is no guarantee that the data behavior will
remain the same beyond the measured range. However, it seems reasonable to
assume that the monthly visits will continue to decline more or less like a
power law, with an exponent $\beta$ not much different from the observed one.
With this assumption, we expect the traffic contribution from the less visited
domains to be minor, especially if $\beta<-1$.

To estimate a systematic uncertainty on the shares of total monthly web
traffic, we fitted a power law up to position
$p_{i}=10(p_{\mathrm{last}}/10)^{i/99}$, with $i=0,\ldots,99$ and
$p_{\mathrm{last}}=254,661$, representing several hypothetical cases where we
have data only for the first $p_{i}$ domains. The smallest and largest $\beta$
obtained, along with the respective power-law scaling factor
$V_{0}=V(p_{\mathrm{last}})/p_{\mathrm{last}}^{\beta}$, were used as
alternative extrapolations to estimate a systematic uncertainty interval on
the total monthly web traffic. The resulting interval for the traffic shares
is shown as a violet band in Fig. 4.

Our analysis reveals a significant concentration of web traffic in a few
domains. Our best estimate suggests that 50% of all web traffic is directed to
the top 3,000 domains, and 80% is allocated to domains in our dataset. Even
our most conservative estimate indicates considerable concentration, with 50%
of the traffic directed to approximately 60,000 domains. This scenario
corresponds to a lower bound on the Gini coefficient – a measure of inequality
commonly used in economic analysis – of 82%, a value comparable to wealth
inequalities observed in countries such as El Salvador, India, Kenya,
Paraguay, and Turkey (Credit Suisse, 2022).

Table 1 presents values for our best traffic extrapolation and for the
systematic uncertainty boundaries with the smallest and largest traffic
concentration, denoted as Sys. $-$ and Sys. $+$. These values are: the power-
law exponent, $\beta$; the total monthly web traffic, $\sum V$; the average
number of domain visits per person per day, $\bar{u}$; the extrapolated
traffic for the last domain in the rank (among 354 million),
$V_{\mathrm{min}}$; the number of top domains that accumulate 50% of total web
traffic, $p_{50\%}$; and the Gini inequality coefficient.

Table 1. Main monthly traffic share attributes under different traffic extrapolations. Extrap. | $\beta$ | $\sum V$ | $\bar{u}$ | $V_{\mathrm{min}}$ | $p_{50\%}$ | Gini
---|---|---|---|---|---|---
Sys. $-$ | -0.83 | $1.14\cdot 10^{12}$ | 7.1 | 341.1 | 62,621 | 82%
Best | -1.19 | $7.81\cdot 10^{11}$ | 4.9 | 27.3 | 2,938 | 97%
Sys. $+$ | -1.52 | $6.98\cdot 10^{11}$ | 4.3 | 2.4 | 1,342 | 99%

The $V_{\mathrm{min}}$ value for the Sys. $-$ boundary is implausibly large,
indicating that the traffic share must decline faster than this rate,
especially towards the end of the traffic rank. Since not all 354 million
domains have an associated website, traffic must decline even faster to reach
a reasonable $V_{\mathrm{min}}$ at an earlier position. Thus, the actual
traffic share is likely closer to our best estimate than to its lower
boundary. The Gini coefficient associated with our best traffic extrapolation
far exceeds the wealth inequality of any country worldwide. The fact that the
domains in our dataset likely represent the destination of 80% of all web
traffic makes them an excellent sample of the current state of the Web, at
least in terms of content access rate.

###  3.3. Popular web industries

SimilarWeb categorizes domains into 210 different industry segments. Some of
these segments are broader categories that encompass others. By considering
only the finer, non-overlapping categories, we are left with 187 industry
segments. We aggregated the monthly visits of domains into these 187
categories to estimate the most popular industries on the Web. This
computation better represents the popularity of topics than others found in
the literature (e.g., (Agarwal and Sastry, 2022)), as it ranks them by monthly
visits rather than by the number of websites. Additionally, it utilizes a
significantly vaster set of domains compared to previous studies.

Figure 5 illustrates that traffic coalesces in a few industries, with 50%
directed to just five segments and 80% directed to 26 segments. Figure 6
depicts the average monthly visits for these sectors. In most cases, despite
the power-law distribution of traffic within industries, monthly visits are
not monopolized by a single domain or company, and the most popular domains
accumulate at most 15% of their industry’s traffic. However, there are
exceptions, as noted below.

Figure 5. Cummulative share of monthly visits aggregated by web industry, from
the most to the least popular.

The cumulative share curve is very smooth, growing rapidly at the start and
flattening out when two-thirds of the industries are accounted for. There is
an “elbow” at industry position 26, where it reaches the cummulative share of
80%.

Figure 6 demonstrates the prevalence of “Search Engines” as the most visited
industry. This prevalence is expected due to the lack of a built-in index on
the Web, making search engines entry points for most users. This trend is
further amplified by the integration of address and search bars in web
browsers, leading users to search even for known websites instead of directly
typing their URLs (Cannon, 2008). In this industry, google.com dominates with
79% of the traffic (considering the top 10,000 domains in the segment). The
following competitors are the Chinese search engine baidu.com, with 4.7%, and
the Russian search engine yandex.ru, with 3.0%.

Figure 6. Monthly visits to the top 26 industries. We shortened some industry
names to save space on the plot.

Bar plot with Search Engines leading the visits. With 1/3 fewer visits, News
and Media appear, followed by TV, movies, and Streaming, then by Social
Networks and Adults. The remaining categories start with half the Adult
visits, and the visits distribution almost flattens out.

With 40% less traffic than “Search Engines,” the next most popular industry is
“News and Media,” which comprises a wide variety of websites, including web
portals such as yahoo.com. Following this is ”TV Movies and Streaming,” where
youtube.com (owned by Alphabet, like google.com) captures 61% of the
industry’s traffic. This segment is succeeded by “Social Networks and Online
Communities,” another case where the industry’s traffic predominantly flows to
a single company. When combining facebook.com, instagram.com, and
whatsapp.com, Meta receives 52% of the segment’s traffic. The last industry in
the top five, before a traffic drop of more than 50%, is “Adult.” Despite its
popularity on the Web, this segment may be underrepresented in surveys and
research where subjects know they are being monitored (Brazilian Network
Information Center, 2022; Crichton et al., 2021). Among the remaining 26
industries listed in Figure 6, only “Dictionaries and Encyclopedias” has a
domain (wikipedia.org) with more than half (56%) of the total industry’s
traffic.

It is essential to observe that domains categorized as “Search Engines,” such
as google.com, yandex.ru, and baidu.com, offer more services than just web
search. Consequently, a portion of the traffic attributed to the “Search
Engines” segment pertains to other industries listed in SimilarWeb’s
classification, such as “Email,” “File Sharing and Hosting,” and “Maps.” While
some of these services are hosted on subdomains and are tracked independently
by SimilarWeb (e.g., news.google.com and cloud.google.com), others are not
(e.g., support.google.com, mail.google.com, and calendar.google.com).
Nevertheless, it is feasible to disregard traffic from all subdomains
(SimilarWeb, 2024a). For google.com, this results in a 15% reduction in
traffic, maintaining the integrity of our rankings. Note that services like
Google Maps are hosted at www.google.com/maps, so their data remains
inseparable from that of google.com.

Considering Google’s widespread popularity and the multitude of services it
offers, it is valuable to assess the significance of each service within the
overall traffic of google.com and its effect on the traffic attributed to the
respective industries. To achieve this, we conducted manual searches on
SimilarWeb to gather data on Google’s subdomains that are neither
independently tracked nor related to search. Fig. 7 illustrates these
findings. By juxtaposing these subdomains’ traffic data with google.com’s
average monthly visits of 85.7 billion, it becomes evident that search
continues to reign as Google’s most frequented service on the Web.

Figure 7. Traffic on Google’s subdomains not tracked independently by
SimilarWeb and unrelated to search.

Horizontal bar plot shows the average monthly visits to Google’s subdomains.
mail.google.com has the most visits: 10.9 billion. It is followed by
docs.google.com (5.5 billion), drive.google.com (2.5 billion),
support.google.com (1.5 billion), calendar.google.com (1.1 billion),
play.google.com (1.1 billion), translate.google.com (694 million), and
meet.google.com (570 million). There are seven more subdomains in the plot.

Nonetheless, Google offers several additional services that would
significantly contribute to its industry’s traffic if SimilarWeb had
independently monitored them. For instance, in the “Email” segment,
mail.google.com received an average of 10.9 billion monthly visits, whereas
the top domain in this segment, live.com, received only 2 billion visits.
Similarly, with 5.6 billion visits, docs.google.com would lead the
“Programming and Developer Software” segment, which includes Software as a
Service (SaaS) platforms like office.com, which received 1.5 billion visits.
Additionally, drive.google.com would lead the “File Sharing and Hosting”
segment with 2.5 billion visits, surpassing the 186 million visits received by
the industry leader, mediafire.com. In the “Computers Electronics and
Technology - Other” category, domains like calendar.google.com,
meet.google.com, and bard.google.com would rank among the top ten most visited
domains. Furthermore, translate.google.com would secure the third position in
the “Dictionaries and Encyclopedias” segment, surpassing the translation
website deepl.com.

###  3.4. Manual inspection of top domains

SimilarWeb data provides no other information about the domains besides
visitation metrics and the domain’s industry. To address this gap, we manually
inspected and researched the 116 most visited domains, which collectively
capture between 22% and 36% of the total web traffic (with a best estimate of
32%). Our objective was to answer the following questions about them:

  1. (1)

Does the website primarily offer SaaS?

  2. (2)

Does the website produce its content? For marketplaces, does the domain owner
sell its own products on the site?

  3. (3)

Does the website function as a platform for user-generated content? For
e-commerce platforms, does the site allow third-party sellers to list their
products? Comments on original content (such as those found on some news
websites) were insufficient to classify the site as a platform. We did not
consider news aggregators or similar sites that curate content from the Web as
platforms since the content is not user-provided.

  4. (4)

Does the website require users to log in before accessing its main content?

  5. (5)

Does the website charge users for access to its main content and features? We
ignored charges for additional features and, for marketplaces, the charges
imposed on sellers.

  6. (6)

Is the domain owner’s primary business or activity related to the Web, or is a
significant portion of their business conducted offline? We considered news
websites primarily web-related, but not e-commerce sites that sell their own
products.

  7. (7)

Who is the ultimate owner of the domain? In the case of subsidiaries, we
identified the ultimate parent company as the domain’s final owner. For
partial ownership, we only considered parent companies that owned at least 50%
of the subsidiaries.

  8. (8)

Is the ultimate owner of the domain a for-profit organization?

  9. (9)

In which country is the domain’s ultimate owner located?

We present our annotations in Tables 2 and 3 (see Appendix A). While the
analysis of this subset of domains does not guarantee a comprehensive
representation of total Web traffic, the trends observed within it are likely
to extend to the following several thousand domains, thus reflecting the
characteristics of a significant portion of Web usage.

Among the top 116 listed domains, Microsoft is the largest owner with 11
domains. These include linkedin.com, openai.com222While we attribute ownership
of OpenAI to Microsoft, the precise nature of their relationship remains a
topic of debate., bing.com, github.com, and several others associated with
Microsoft itself and its SaaS products like office365.com. Following is
Amazon, the owner of twitch.tv, imdb.com, and five Amazon-related domains.
Alphabet holds ownership of five domains, including youtube.com and various
Google domains, while Meta possesses four (facebook.com, instagram.com,
whatsapp.com, and messenger.com). The first non-US company in terms of top
domains is the Russian VK, which owns dzen.ru, vk.com, mail.ru, and ok.ru,
followed by the Japanese SoftBank Group Corp., which owns three domains.

Suppose we rank the final owners by their aggregated traffic. In that case,
Alphabet receives 120 billion visits monthly, accounting for almost 50% of the
total traffic of the top 116 domains (see Fig. 8). This ranking also roughly
follows a power-law distribution, but its best-fit exponent, $\beta$, is more
negative than the best-fit for the disaggregated top 116 domains: -1.22 versus
-0.98. It is important to note that this decrease in $\beta$ is not an
artifact caused by the aggregation itself. Randomly aggregating domains would
typically blend famous and less known domains, evening out the traffic and
resulting in a less negative $\beta$. Thus, there must be a socio-economic
explanation for why the same companies own highly visited domains. Although
advancing such an explanation is beyond the scope of this paper, we conjecture
that this phenomenon partially stems from large companies acquiring popular
websites (e.g., youtube.com, linkedin.com, dzen.ru, twitch.tv, imdb.com, and
github.com were independent websites later purchased by their current owners).

Figure 8. Fraction of the top 116 domains’ total traffic aggregated by final
owner. The plot shows only the top 15 final owners.

Horizontal bar plot. Alphabet is by far the owner with the most visits,
reaching 49% of the top domains’ total traffic. Meta comes in second, with
11%. Then we have Microsoft at 5%, Elon Musk at 2.5%, Amazon at 2.3%, and
Baidu at 2.1%.

When we aggregate the top domains’ visits by the ultimate owner’s country, the
concentration becomes even more pronounced. The best-fit power-law exponent
reaches $\beta=-2.00$, with the United States capturing 80% of the total
traffic of the top 116 domains. This dominance is followed by China (4.5%),
Russia (3.7%), Japan (2.6%), the Czech Republic (2.1%), and South Korea
(0.97%).

Fig. 9 summarizes the traffic shares associated with the binary answers to the
remaining questions (1 to 6 and 8). Each bar in the plot illustrates how the
traffic is distributed among domains subjected to a specific binary
classification. Notably, the vast majority (97%) of the traffic to the top 116
domains flows to domains owned by profit-seeking companies. Interestingly,
among these 116 domains, only two are not-for-profit: wikipedia.org and
archiveofourown.org. This observation is intriguing, especially considering
that 96% of the top traffic goes to websites that do not charge for access or
their main functionalities. This seeming contradiction underscores emerging
business models that rely on charging for additional features (e.g., the
Freemium model (Georgieva et al., 2015)), growing the user base for a later
sell-out (Lee et al., 2006), and operating two-sided markets where users are
subsidized to facilitate data collection, advertising, and sales for the other
market side (Hermes et al., 2020b). Additionally, the figure reveals that 94%
of the visits stream to web-related domains, indicating businesses with no
offline counterpart (e.g., search engines, online advertising, marketplaces,
social media, SaaS).

Figure 9. Fraction of the top 116 domains’ total traffic directed to domains
classified under seven binary properties. The gray segments denote traffic
directed to unclassified domains. Segments marked with * would be larger
depending on how we deal with large search engines (see text).

Each bar shows the traffic share directed to the domains’ classes in a binary
classification. The plot shows that more than 90% of the top 116 domains’
traffic is directed to domains owned by for-profit companies, that are free to
access, that are nos SaaS, and that are mainly related to an online business
(with no “offline” counterpart). We also see that about 85% of the traffic
share goes to domains that do not produce their own content, and almost 80% do
not require a login. About 47% are platforms where users post their content.

Figure 9 emphasizes that 85% of the total traffic to top domains flows to
platforms that do not generate their content, such as social media platforms,
marketplaces, news aggregators, and search engines. Conversely, 46% of the
traffic goes to websites where users themselves create content. Excluding
traffic to search engines, which neither produce content nor allow users to
post content, the proportion of traffic allocated to user-generated content
platforms rises to 77%, with minimal impact on the distribution across other
domain categorizations. This large percentage underscores the central role of
user-content platforms in the Web’s contemporary landscape.

Finally, the analysis reveals that SaaS does not emerge as a prominent traffic
driver for top domains. This fact suggests that Web visitors prioritize
content over services, indicating a lesser emphasis on the Web as a software
platform. However, as detailed in Section 3.3, notable domains like
google.com, yandex.ru, baidu.com, amazon.com, and yahoo.com offer additional
services beyond their primary functions, some of which fall under the SaaS
category. For instance, approximately 15% of Google’s traffic is directed to
subdomains primarily recognized as SaaS (refer to Fig. 7). A similar scenario
unfolds regarding the necessity for user authentication to access the Web:
while 76% of the traffic to top domains goes to websites categorized as not
requiring login credentials, a considerable portion of this traffic flows to
domains with subdomains demanding authentication. Thus, at least 24% of this
traffic necessitates user authentication.

We employed clustering techniques to categorize the top 116 domains into four
groups. We aimed to minimize the average Hamming distances within each group
across a 7-dimensional binary feature space constructed from our questionnaire
responses. The domains within each cluster exhibit similar characteristics,
allowing us to delineate the clusters using the following archetypes:

  1. (1)

_SaaS_ : This group comprises domains primarily characterized by SaaS
offerings, devoid of offline activities and requiring user authentication.
While some domains charge users for their core services, many do not. Examples
include openai.com, zoom.us, office365.com, and canva.com. Our clustering
process assigned 12 domains to this group.

  2. (2)

_Open content providers_ : Domains in this cluster serve as content producers,
offering access to their content without imposing a paywall or requiring user
authentication. Notably, this is the only group to include businesses with
offline operations and closely resembles the ethos of the early internet era,
often referred to as “Web 1.0.” Examples encompass samsung.com, walmart.com,
bbc.co.uk, weather.com, and yahoo.com. Amazon.com was categorized within this
group by our clustering algorithm, along with other 31 domains.

  3. (3)

_Platforms_ : This cluster encompasses domains whose activities are confined
to the online realm, where users contribute the entirety of the content, and
access to the websites is free. Prominent examples comprise youtube.com,
roblox.com, chaturbate.com, tiktok.com, wikipedia.org, and booking.com.
Interestingly, search engines and messaging web services such as telegram.org
and discord.com were assigned to this group by our clustering method. In
total, 67 domains were classified under this archetype.

  4. (4)

_Subscription content providers_ : Domains in this category are online content
producers that mandate user authentication and levy charges for accessing
their content. Examples include nytimes.com, espn.com, netflix.com, and
disneyplus.com. Our clustering process identified five domains within this
group.

Figure 10 shows that the cluster designated as “Platforms” is the primary
destination for traffic among the top 116 domains, even when excluding search
engines from consideration. While some traffic directed toward search engines
could be labeled as SaaS due to their supplementary services, the preeminence
of the Platforms cluster remains unassailable.

Figure 10. Fraction of the top 116 domains’ total traffic directed to each of
the 4 clusters. Search engines were manually removed from cluster group
“Platforms”.

Horizontal bar plot. 44% of the top 116 domains’ traffic goes to Platforms.
This is followed by search engines (40%), Open content providers (10%), SaaS
(4.3%), and Subscription content providers (1.6%).

##  4\. Summary and conclusions

Our analysis commenced with examining the month-to-month fluctuations in
websites’ monthly visit counts to assess the stability of website traffic
rankings (Section 3.1). We observed that these fluctuations can be
substantial, mainly due to the presence of outliers (see Fig. 1). However, the
ranking remains relatively stable in the short term, largely owing to its
power-law-like distribution segregating websites by orders of magnitude (refer
to Fig. 3). Notably, our traffic evaluation represents, to the best of our
knowledge, the most comprehensive assessment published to date, encompassing
data from 254,661 domains.

By utilizing a power-law fit to extrapolate monthly visits from rank position
254,661 to encompass the estimated 354 million existing domains as of 2023
(Verisign, 2023), we projected total web traffic to reach 781 billion visits
per month (Section 3.2). This projection corresponds to an average of 4.9
visits per user per day, a figure broadly in line with existing estimates
(Crichton et al., 2021; Petrosyan, 2024). Such an extrapolation enabled us to
gauge the degree of concentration of web usage within a small subset of top
websites. Our analysis suggests that approximately 50% of all web traffic is
directed towards roughly 3,000 websites, with 80% of the total traffic
accounted for by the websites in our dataset (refer to Fig. 4). This degree of
concentration is remarkably high, as indicated by a Gini coefficient of 97%
(see Table 1).

To the best of our knowledge, our estimation of web usage concentration is the
only published one to date. Its implications for web studies are profound, as
it underscores, through quantitative data, that the characteristics of the
Web, particularly in terms of usage, can be reasonably inferred from a minimal
fraction of all registered domains. While our dataset covers a mere 0.07% of
all domains, it is likely to encapsulate around 80% of total web traffic.

Using this dataset, we revealed an accentuated concentration of user interest
over a limited number of industries (refer to Fig. 5). Specifically, we found
that half of the traffic in our dataset flows toward five industries: “Search
Engines,” “News and Media,” “TV Movies and Streaming,” “Social Networks and
Online Communities,” and “Adult” (see Fig. 6). Previous studies examining user
interests have often relied on smaller, biased samples (Crichton et al.,
2021). It is worth noting that certain top domains, such as google.com,
yandex.ru, baidu.com, amazon.com, and yahoo.com, encompass multiple services
that could be classified under different industries than their primary ones.
For instance, an analysis of Google’s traffic reveals that approximately 15%
of it is directed to subdomains, with a significant portion associated with
SaaS, particularly in functionalities like email, document editing, and file
storage (see Fig. 7). Consequently, the actual usage attributed to search
engines may be slightly lower, while the utilization of SaaS-related
industries may be slightly higher than depicted in Fig. 6.

Our in-depth analysis of the 116 most visited domains, collectively
responsible for approximately one-third of all web traffic, reveals that
nearly all of this traffic goes towards websites owned by for-profit digital
tech companies. This observation highlights the Web primarily as a vehicle for
commercial enterprise. Notably, this enterprise predominantly manifests as
purely online ventures rather than online extensions of offline activities.
Originally conceived as an open and uncharted domain, the Web has evolved into
a realm ripe for commercial exploitation.

Furthermore, it is noteworthy that almost all of the 116 domains analyzed do
not charge users for access or the core functionalities offered by them. This
lack of paywalls highlights prevalent business models on the Web, which rely
on alternative revenue streams. Such revenue generation methods may include
charging for supplementary features, as observed in domains like google.com,
youtube.com, roblox.com, amazon.com, and github.com. Additionally, some
companies secure venture capital funding to pursue a “growth at all costs”
strategy, as exemplified by platforms such as whatsapp.com and quora.com
(Kutcher et al., 2014; Ernst & Young LLP, 2014; Constine, 2013). Moreover,
certain companies engage in two-sided markets, where the user base serves more
as an asset than traditional customers, providing valuable data, customers,
and audiences for the other side of the market. Prominent examples of this
approach include facebook.com, booking.com, ebay.com, and youtube.com (Zuboff,
2019).

Most traffic to top domains goes towards websites that do not generate their
content. Instead, this content originates from various sources, including
users (e.g., instagram.com, xvideos.com, github.com, and messenger.com),
sellers (ebay.com, booking.com, and aliexpress.com), or other external
websites (such as search engines and news aggregators). We refer to websites
that exhibit these predominant characteristics as “Platforms.” Platforms
compose most of the top domains (67 out of 116) and collectively capture over
80% of its traffic (refer to Fig. 10), underscoring their significance in
scholarly discourse (Hein et al., 2020; Hermes et al., 2020b; Zuboff, 2019).

While our platform identification efforts focused on approximately one-third
of the web traffic, our data suggests that platform hegemony could extend to
more significant portions of web usage. Several prominent industries outlined
in Fig. 6 primarily consist of such websites. For instance, “Search Engines,”
“Social Networks and Online Communities,” and “Adult” industries are mostly
comprised of platforms (refer to Tables 2 and 3). Additionally, “TV Movies and
Streaming,” although primarily constituted by subscription content providers,
sees its traffic largely dominated by youtube.com, a platform. While platform
dominance has been previously acknowledged in the financial realm (Evans and
Gawer, 2016) and may be readily observed through personal experience, we are
unaware of former quantitative evidence of its prevalence over web traffic.

The concentration of web visits on platforms can be seen as a form of
privatization of the Web, particularly concerning its usage dynamics. Despite
the abundance of domain names and websites – publishing an independent website
remains straightforward and accessible to many – visits are a scarce resource
that platforms have enclosed. Within the web context, visits represent more
than mere audience numbers, as visitors actively contribute to the content
hosted on platforms. Web usage, encompassing activities such as viewing,
posting, and interacting, predominantly occurs within these platforms, which
dictate the rules governing these activities and determine the fate of the
content and data generated through these visits.

Our analysis of the top 116 domains also reveals that at least 24% of their
visits necessitate user authentication, indicating that while the Web appears
largely open, a noticeable portion operates behind login barriers.
Furthermore, our findings suggest that other uses than SaaS may predominate on
the Web. However, rather than relying on a web survey like ours, a dedicated
application survey would be necessary to ascertain whether applications are
transitioning to the Web.

Additionally, we observe that a staggering 80% of the traffic to the top
domains flows to companies in the United States. This substantial
concentration, which could potentially characterize the Web as predominantly
an American enterprise, has been previously highlighted in terms of companies’
market value (Evans and Gawer, 2016), but not in terms of traffic
distribution. As discussed in Section 3.4, the accumulation of traffic on
specific companies necessitates a socio-economic and political explanation.
Hermes _et al._ took a step in this direction by interviewing European experts
and top managers to glean insights into the reasons behind the dominance of
American platforms in terms of market value (Hermes et al., 2020a). Their
findings suggest that factors such as a results-oriented mindset, willingness
to take risks, a sizable domestic market, state investment, early-mover
advantage, the establishment of technology hubs (such as Silicon Valley),
close collaborations with universities, and access to venture capital
collectively contribute to American dominance in the web landscape.

The utilization of the Web exhibits, of course, significant diversity, varying
among different individuals, social groups, and countries. However, our
analysis indicates that, currently, on a global scale, the Web is
predominantly characterized by the overwhelming presence of for-profit
American platforms with business models not reliant on subscription fees. This
observation provides quantitative support for common descriptions of the
digital realm, such as “Platform capitalism” (Srnicek, 2016), “Surveillance
capitalism” (Zuboff, 2019), and “Technofeudalism” (Varoufakis, 2024). While
these descriptions often focus on corporate giants like Alphabet, Meta,
Microsoft, and Amazon, potentially conveying that their findings are specific
to these companies, our analysis suggests otherwise. These companies not only
command a significant portion of web usage but also exhibit characteristics
mirrored by numerous smaller entities, indicating that their descriptions may
reflect broader trends across the web ecosystem.

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##  Appendix A Annotated data

Tables 2 and 3 provide data about the top 116 domains in terms of web traffic.
The first three columns present information from SimilarWeb: the domain name,
its corresponding industry (with some industry names abbreviated for table
readability), and the average monthly visit count (measured in billions),
denoted as $V$. Subsequent columns, except for the last one, were manually
annotated based on the questions outlined in Section 3.4, with binary
responses marked as 1 for ”yes” and 0 for ”no.”

Tables 2 and 3 provide data about the top 116 domains in terms of web traffic.
The first three columns present information from SimilarWeb: the domain name,
its corresponding industry (with some industry names abbreviated for table
readability), and the average monthly visit count (measured in billions),
denoted as $V$. Subsequent columns, except for the last one, were manually
annotated based on the questions outlined in Section 3.4, with binary
responses marked as 1 for ”yes” and 0 for ”no.”

The first three specify if we considered the respective domain: a SaaS
provider (question 1); a content producer, $CP$ (question 2); and a platform
for user-generated content, $Pl$ (question 3). The following three columns
inform if the domain: requires user login, $Log$ (question 4); charges for
primary usage or access, $Ch$ (question 5); and primarily operates as a web-
related business (question 6). Subsequent columns include information on the
final owner of the domain according to Wikipedia (question 7, with some owner
names abbreviated for table presentation), whether the owner is a for-profit
organization, $\$$ (question 8), and the country of origin of the owner
(question 9, with some country names abbreviated or transformed into acronyms
for table presentation). The last column indicates the cluster assignment (out
of four) determined through unsupervised agglomerative clustering. We defined
the cluster descriptors after examining the prevailing characteristics of each
cluster: ”Plat” for platforms; ”OCP” for open content providers; ”SaaS” for
software as a service; and ”SCP” for subscription content providers.

Domain | Industry | V ($10^{9}$) | SaaS | CP | Pl | Log | Ch | Web | Owner | $ | Country | Cl
---|---|---|---|---|---|---|---|---|---|---|---|---
google.com | Search Engines | 85.730 | 0 | 0 | 0 | 0 | 0 | 1 | Alphabet | 1 | USA | Plat
youtube.com | TV Movies & Streaming | 32.810 | 0 | 0 | 1 | 0 | 0 | 1 | Alphabet | 1 | USA | Plat
facebook.com | Social Networks | 16.810 | 0 | 0 | 1 | 1 | 0 | 1 | Meta | 1 | USA | Plat
instagram.com | Social Networks | 6.602 | 0 | 0 | 1 | 1 | 0 | 1 | Meta | 1 | USA | Plat
twitter.com | Social Networks | 6.143 | 0 | 0 | 1 | 1 | 0 | 1 | Elon Musk | 1 | USA | Plat
baidu.com | Search Engines | 5.095 | 0 | 0 | 0 | 0 | 0 | 1 | Baidu | 1 | China | Plat
wikipedia.org | Dictionaries & Encyclopedias | 4.413 | 0 | 0 | 1 | 0 | 0 | 1 | Wikimedia | 0 | USA | Plat
yahoo.com | News & Media | 3.747 | 0 | 1 | 0 | 0 | 0 | 1 | Apollo | 1 | USA | OCP
yandex.ru | Search Engines | 3.225 | 0 | 0 | 0 | 0 | 0 | 1 | Yandex | 1 | Russia | Plat
whatsapp.com | Social Networks | 3.033 | 1 | 0 | 1 | 1 | 0 | 1 | Meta | 1 | USA | Plat
xvideos.com | Adult | 2.676 | 0 | 0 | 1 | 0 | 0 | 1 | WGCZ | 1 | Czech Rep. | Plat
amazon.com | Marketplace | 2.394 | 0 | 1 | 1 | 0 | 0 | 0 | Amazon | 1 | USA | OCP
pornhub.com | Adult | 2.273 | 0 | 0 | 1 | 0 | 0 | 1 | Ethical Capital Partners | 1 | Canada | Plat
tiktok.com | Social Networks | 2.235 | 0 | 0 | 1 | 0 | 0 | 1 | ByteDance | 1 | China | Plat
xnxx.com | Adult | 2.199 | 0 | 0 | 1 | 0 | 0 | 1 | WGCZ | 1 | Czech Rep. | Plat
yahoo.co.jp | News & Media | 2.035 | 0 | 1 | 0 | 0 | 0 | 1 | SoftBank | 1 | Japan | OCP
live.com | Email | 2.019 | 1 | 0 | 0 | 1 | 0 | 1 | Microsoft | 1 | USA | SaaS
reddit.com | Social Networks | 1.863 | 0 | 0 | 1 | 0 | 0 | 1 | Reddit | 1 | USA | Plat
docomo.ne.jp | Telecommunications | 1.801 | 0 | 1 | 0 | 0 | 0 | 0 | NTT | 1 | Japan | OCP
linkedin.com | Social Networks | 1.780 | 0 | 0 | 1 | 1 | 0 | 1 | Microsoft | 1 | USA | Plat
openai.com | Computers & Tech - Other | 1.561 | 1 | 1 | 0 | 1 | 0 | 1 | Microsoft | 1 | USA | SaaS
office.com | Prog. & Dev. Software | 1.541 | 1 | 0 | 0 | 1 | 0 | 1 | Microsoft | 1 | USA | SaaS
xhamster.com | Adult | 1.519 | 0 | 0 | 1 | 0 | 0 | 1 | Hammy Media | 1 | Cyprus | Plat
netflix.com | TV Movies & Streaming | 1.453 | 0 | 1 | 0 | 1 | 1 | 1 | Netflix | 1 | USA | SCP
dzen.ru | Community & Society - Other | 1.394 | 0 | 0 | 1 | 0 | 0 | 1 | VK | 1 | Russia | Plat
microsoftonline.com | Prog. & Dev. Software | 1.239 | 1 | 0 | 0 | 1 | 0 | 1 | Microsoft | 1 | USA | SaaS
bing.com | Search Engines | 1.238 | 0 | 0 | 0 | 0 | 0 | 1 | Microsoft | 1 | USA | Plat
samsung.com | Consumer Electronics | 1.230 | 0 | 1 | 0 | 0 | 0 | 0 | Samsung | 1 | South Korea | OCP
bilibili.com | Animation & Comics | 1.200 | 0 | - | 1 | 0 | 0 | 1 | Bilibili | 1 | China | Plat
vk.com | Social Networks | 1.197 | 0 | 0 | 1 | 1 | 0 | 1 | VK | 1 | Russia | Plat
naver.com | News & Media | 1.177 | 0 | 0 | 1 | 0 | 0 | 1 | Naver | 1 | South Korea | Plat
mail.ru | Email | 1.170 | 1 | 1 | 1 | 1 | 0 | 1 | VK | 1 | Russia | SaaS
weather.com | Weather | 1.123 | 0 | 1 | 0 | 0 | 0 | 0 | IBM | 1 | USA | OCP
pinterest.com | Social Networks | 1.082 | 0 | 0 | 1 | 0 | 0 | 1 | Pinterest | 1 | USA | Plat
discord.com | Social Networks | 1.069 | 1 | 0 | 1 | 1 | 0 | 1 | Discord | 1 | USA | Plat
twitch.tv | Video Games | 1.038 | 0 | 0 | 1 | 0 | 0 | 1 | Amazon | 1 | USA | Plat
turbopages.org | News & Media | 0.995 | - | - | - | - | - | - | - | - | - | Plat
max.com | TV Movies & Streaming | 0.980 | 0 | 1 | 0 | 1 | 1 | 1 | Warner Bros. Discovery | 1 | USA | SCP
microsoft.com | Prog. & Dev. Software | 0.978 | 0 | 1 | 0 | 0 | 0 | 0 | Microsoft | 1 | USA | OCP
zoom.us | Computers & Tech - Other | 0.830 | 1 | 0 | 0 | 1 | 0 | 1 | Zoom | 1 | USA | SaaS
duckduckgo.com | Search Engines | 0.806 | 0 | 0 | 0 | 0 | 0 | 1 | DuckDuckGo | 1 | USA | Plat
qq.com | News & Media | 0.791 | 0 | - | 1 | 0 | 0 | 1 | Tencent | 1 | China | Plat
xhamster.desi | Adult | 0.776 | 0 | 0 | 1 | 0 | 0 | 1 | Hammy Media | 1 | Cyprus | Plat
roblox.com | Video Games | 0.761 | 0 | 0 | 1 | 1 | 0 | 1 | Roblox | 1 | USA | Plat
stripchat.com | Adult | 0.737 | 0 | 0 | 1 | 0 | 0 | 1 | StripChat | 1 | USA | Plat
quora.com | Dictionaries & Encyclopedias | 0.729 | 0 | 0 | 1 | 1 | 0 | 1 | Quora | 1 | USA | Plat
fandom.com | Arts & Entertainment - Other | 0.727 | 0 | 1 | 1 | 0 | 0 | 1 | TGP | 1 | USA | OCP
globo.com | News & Media | 0.723 | 0 | 1 | 0 | 0 | 0 | 1 | Globo | 1 | Brazil | OCP
sharepoint.com | Computers & Tech - Other | 0.704 | 1 | 0 | 0 | 1 | 1 | 1 | Microsoft | 1 | USA | SaaS
t.me | Computers & Tech - Other | 0.697 | 1 | 0 | 1 | 1 | 0 | 1 | Telegram | 1 | UAE | Plat
ebay.com | Marketplace | 0.688 | 0 | 0 | 1 | 0 | 0 | 1 | Ebay | 1 | USA | Plat
msn.com | News & Media | 0.665 | 0 | 0 | 1 | 0 | 0 | 1 | Microsoft | 1 | USA | Plat
news.yahoo.co.jp | News & Media | 0.640 | 0 | 1 | 0 | 0 | 0 | 1 | SoftBank | 1 | Japan | OCP
cnn.com | News & Media | 0.622 | 0 | 1 | 0 | 0 | 0 | 1 | Warner Bros. Discovery | 1 | USA | OCP
indeed.com | Jobs & Employment | 0.612 | 0 | 0 | 1 | 1 | 0 | 1 | Recruit Holdings | 1 | Japan | Plat
nytimes.com | News & Media | 0.610 | 0 | 1 | 0 | 1 | 1 | 1 | The New York Times | 1 | USA | SCP
booking.com | Accommodation & Hotels | 0.583 | 0 | 0 | 1 | 0 | 0 | 1 | Booking Holdings | 1 | USA | Plat
google.com.br | Search Engines | 0.579 | 0 | 0 | 0 | 0 | 0 | 1 | Alphabet | 1 | USA | Plat

Table 2. First half of the top 116 domains in terms of average monthly visits,
along with our annotations and unsupervised cluster classification.

Domain | Industry | V ($10^{9}$) | SaaS | CP | Pl | Log | Ch | Web | Owner | $ | Country | Cl
---|---|---|---|---|---|---|---|---|---|---|---|---
bbc.co.uk | News & Media | 0.578 | 0 | 1 | 0 | 0 | 0 | 1 | BBC | 1 | UK | OCP
spankbang.com | Adult | 0.574 | 0 | 0 | 1 | 0 | 0 | 1 | StripChat | 1 | USA | Plat
amazon.co.jp | Marketplace | 0.570 | 0 | 1 | 1 | 0 | 0 | 0 | Amazon | 1 | USA | OCP
aajtak.in | News & Media | 0.557 | 0 | 1 | 0 | 0 | 0 | 1 | Living Media India | 1 | India | OCP
ok.ru | Social Networks | 0.551 | 0 | 0 | 1 | 1 | 0 | 1 | VK | 1 | Russia | Plat
accuweather.com | Weather | 0.547 | 0 | 1 | 0 | 0 | 0 | 0 | AccuWeather | 1 | USA | OCP
zhihu.com | Social Networks | 0.532 | 0 | 0 | 1 | 1 | 0 | 1 | Zhihu | 1 | China | Plat
espn.com | Sports - Other | 0.528 | 0 | 1 | 0 | 1 | 1 | 1 | Walt Disney Company | 1 | USA | SCP
bbc.com | News & Media | 0.522 | 0 | 1 | 0 | 0 | 0 | 1 | BBC | 1 | UK | OCP
rakuten.co.jp | Marketplace | 0.507 | 0 | - | 1 | 0 | - | - | Rakuten | 1 | Japan | Plat
canva.com | Multimedia & Web Design | 0.500 | 1 | 0 | 0 | 1 | 0 | 1 | Canva | 1 | Australia | SaaS
apple.com | Consumer Electronics | 0.492 | 0 | 1 | 0 | 0 | 0 | 0 | Apple | 1 | USA | OCP
imdb.com | TV Movies & Streaming | 0.488 | 0 | 0 | 1 | 0 | 0 | 1 | Amazon | 1 | USA | Plat
ya.ru | Search Engines | 0.484 | 0 | 0 | 0 | 0 | 0 | 1 | Yandex | 1 | Russia | Plat
spotify.com | Music | 0.480 | 0 | 1 | 1 | 1 | 0 | 1 | Spotify | 1 | Sweden | OCP
ssyoutube.com | Search Engines | 0.475 | 1 | 0 | 0 | 0 | 0 | 1 | - | - | - | SaaS
instructure.com | Education | 0.474 | 1 | 1 | 0 | 0 | 0 | 0 | Thoma Bravo | 1 | USA | OCP
uol.com.br | News & Media | 0.472 | 0 | 1 | 0 | 0 | 0 | 1 | Grupo Folha | 1 | Brazil | OCP
cricbuzz.com | Fantasy Sports | 0.469 | 0 | 1 | 0 | 0 | 0 | 1 | The Times Group | 1 | India | OCP
etsy.com | Marketplace | 0.466 | 0 | 0 | 1 | 0 | 0 | 1 | Etsy | 1 | USA | Plat
yiyouliao.com | Prog. & Dev. Software | 0.465 | 1 | 0 | 0 | 1 | 1 | 1 | Youliang Technology | 1 | China | SaaS
aliexpress.com | Marketplace | 0.454 | 0 | 0 | 1 | 0 | 0 | 1 | Alibaba Group | 1 | China | Plat
paypal.com | Banking Credit & Lending | 0.452 | 0 | 0 | 0 | 1 | 1 | 1 | Paypal | 1 | USA | SaaS
amazon.de | Marketplace | 0.439 | 0 | 1 | 1 | 0 | 0 | 0 | Amazon | 1 | USA | OCP
chaturbate.com | Adult | 0.427 | 0 | 0 | 1 | 0 | 0 | 1 | Multi Media LLC | 1 | USA | Plat
github.com | Prog. & Dev. Software | 0.425 | 0 | 0 | 1 | 1 | 0 | 1 | Microsoft | 1 | USA | Plat
dailymail.co.uk | News & Media | 0.412 | 0 | 1 | 0 | 0 | 0 | 1 | Daily Mail & General Trust | 1 | UK | OCP
google.de | Search Engines | 0.405 | 0 | 0 | 0 | 0 | 0 | 1 | Alphabet | 1 | USA | Plat
ozon.ru | Marketplace | 0.403 | 0 | 1 | 1 | 0 | 0 | 0 | Ozon | 1 | Russia | OCP
taobao.com | Marketplace | 0.400 | 0 | - | 1 | 1 | - | 0 | Alibaba Group | 1 | China | Plat
walmart.com | Marketplace | 0.395 | 0 | 1 | 1 | 0 | 0 | 0 | Walton family | 1 | USA | OCP
news.google.com | News & Media | 0.392 | 0 | 0 | 0 | 0 | 0 | 1 | Alphabet | 1 | USA | Plat
wildberries.ru | Marketplace | 0.391 | 0 | 1 | 1 | 0 | 0 | 0 | Wildberries | 1 | Russia | OCP
avito.ru | Classifieds | 0.378 | 0 | 0 | 1 | 0 | 0 | 1 | Kismet Capital Group | 1 | Russia | Plat
miguvideo.com | TV Movies & Streaming | 0.365 | 0 | - | - | - | - | 1 | - | - | - | Plat
amazon.co.uk | Marketplace | 0.364 | 0 | 1 | 1 | 0 | 0 | 0 | Amazon | 1 | USA | OCP
theguardian.com | News & Media | 0.352 | 0 | 1 | 0 | 0 | 0 | 1 | Scott Trust Limited | 1 | UK | OCP
tripmall.online | Ecommerce - Other | 0.348 | 0 | 0 | 0 | 0 | 0 | 1 | 12 Co Ltd | 1 | Japan | Plat
marca.com | Sports - Other | 0.341 | 0 | 1 | 0 | 0 | 0 | 1 | Cairo Communication | 1 | Italy | OCP
pemsrv.com | Adult | 0.338 | - | - | - | - | - | - | - | - | - | Plat
messenger.com | Social Networks | 0.338 | 1 | 0 | 1 | 1 | 0 | 1 | Meta | 1 | USA | Plat
amazon.in | Marketplace | 0.336 | 0 | 1 | 1 | 0 | 0 | 0 | Amazon | 1 | USA | OCP
aniwave.to | Animation & Comics | 0.334 | 0 | 0 | 0 | 0 | 0 | 1 | - | - | - | Plat
pixiv.net | Visual Arts & Design | 0.330 | 0 | 0 | 1 | 1 | 0 | 1 | Pixiv | 1 | Japan | Plat
archiveofourown.org | Books & Literature | 0.327 | 0 | 0 | 1 | 0 | 0 | 1 | Org. for Transf. Works | 0 | USA | Plat
pinduoduo.com | Marketplace | 0.325 | 0 | 0 | 1 | 1 | 0 | 1 | PDD Holdings | 1 | Ireland | Plat
aol.com | Computers & Tech - Other | 0.324 | 0 | 0 | 0 | 0 | 0 | 1 | Apollo | 1 | USA | Plat
temu.com | Marketplace | 0.321 | 0 | 0 | 1 | 1 | 0 | 1 | PDD Holdings | 1 | Ireland | Plat
zillow.com | Real Estate | 0.320 | 1 | 1 | 1 | 0 | - | 0 | Zillow | 1 | USA | OCP
line.me | Social Networks | 0.314 | 1 | 0 | 1 | 1 | 0 | 1 | SoftBank | 1 | Japan | Plat
disneyplus.com | TV Movies & Streaming | 0.313 | 0 | 1 | 0 | 1 | 1 | 1 | Walt Disney Company | 1 | USA | SCP
google.co.jp | Search Engines | 0.311 | 0 | 0 | 0 | 0 | 0 | 1 | Alphabet | 1 | USA | Plat
onlyfans.com | Adult | 0.308 | 0 | 0 | 1 | 1 | 1 | 1 | Fenix International Ltd | 1 | UK | Plat
office365.com | Prog. & Dev. Software | 0.301 | 1 | 0 | 0 | 1 | 1 | 1 | Microsoft | 1 | USA | SaaS
telegram.org | Social Networks | 0.296 | 1 | 0 | 1 | 1 | 0 | 1 | Telegram | 1 | UAE | Plat
foxnews.com | News & Media | 0.294 | 0 | 1 | 0 | 1 | 0 | 1 | Murdoch family | 1 | USA | OCP
tsyndicate.com | Marketing & Advertising | 0.290 | 0 | 0 | 0 | 0 | 0 | 1 | - | - | - | Plat
seznam.cz | Search Engines | 0.287 | 0 | 0 | 0 | 0 | 0 | 1 | Seznam | 1 | Czech Rep. | Plat

Table 3. Second half of the top 116 domains in terms of average monthly
visits, along with our annotations and unsupervised cluster classification.