# Understanding the Impact of AI Generated Content on Social Media: The Pixiv
Case

Yiluo Wei  Hong Kong University of Science and Technology (GZ)China and
Gareth Tyson  Hong Kong University of Science and Technology (GZ)China

(2023)

###### Abstract.

In the last two years, Artificial Intelligence Generated Content (AIGC) has
received significant attention, leading to an anecdotal rise in the amount of
AIGC being shared via social media platforms. The impact of AIGC and its
implications are of key importance to social platforms, e.g., regarding the
implementation of policies, community formation, and algorithmic design. Yet,
to date, we know little about how the arrival of AIGC has impacted the social
media ecosystem. To fill this gap, we present a comprehensive study of _Pixiv_
, an online community for artists who wish to share and receive feedback on
their illustrations. Pixiv hosts over 100 million artistic submissions and
receives more than 1 billion page views per month (as of 2023). Importantly,
it allows both human and AI generated content to be uploaded. Exploiting this,
we perform the first analysis of the impact that AIGC has had on the social
media ecosystem, through the lens of Pixiv. Based on a dataset of 15.2 million
posts (including 2.4 million AI-generated images), we measure the impact of
AIGC on the Pixiv community, as well as the differences between AIGC and
human-generated content in terms of content creation and consumption patterns.
Our results offer key insight to how AIGC is changing the dynamics of social
media platforms like Pixiv.

††copyright: acmcopyright††journalyear: 2023††doi: XXXXXXX.XXXXXXX

##  1\. Introduction

Artificial Intelligence Generated Content (AIGC) refers to content that is
produced using generative AI techniques, rather than being authored by humans.
In recent years, generative AI techniques have evolved significantly. Since,
society has become intrigued by an array of content generation products, such
as ChatGPT (cha, 2023) for text and Midjourney (mid, 2023) for images,
particularly as they enable the automated creation of large volumes of content
in a short period of time. Preliminary evidence suggests that this has led to
an increase in the amount of AIGC content being shared via online social
platforms (Yang and Menczer, 2023), triggering debate regarding aesthetic
(Roose, 2022; Hitsuwari et al., 2023; Demmer et al., 2023), ethical (Liebrenz
et al., 2023; Lund et al., 2023; Kooli, 2023; Naik et al., 2022), and legal
(Hacker et al., 2023; Chen et al., 2023; Naik et al., 2022) issues. AI-
generated images, in particular, have garnered significant attention, due to
their impressive ability to simulate photographs and art.

Whereas studies have been conducted to investigate people’s perceptions of AI-
generated art (Ragot et al., 2020), the impact of AIGC on online social media
platforms is yet to be studied. We argue that the exploration of AIGC on
social media is crucial from three perspectives. (i) _Policies of Social Media
Platforms:_ AIGC offers convenience and speed, yet also holds the potential to
amplify harmful narratives and drown out alternative opinions. Platforms need
to comprehend the implications of AIGC and the trade-offs involved to
implement the most appropriate guidelines and policies. (ii) _Community
Formation:_ Creators and consumers of AIGC may differ significantly from more
traditional human-generated content communities. While including them in
traditional creator communities may enhance diversity, it poses the risk of
introducing issues that could be harmful to the community. For example, the
scale at which AIGC can generate social media posts may lead to other members
leaving the community (lea, 2023). (iii) _Algorithms and Engagement:_ Social
media platforms utilize algorithms to recommend content to users. By
discerning the differences between AIGC and human-generated content, platforms
can fine-tune their strategies, thereby ensuring that models are not
negatively impacted by the volume of data created by AIGC. We therefore argue
it is vital to better understand how AIGC impacts platforms and communities,
and how the creation and consumption of AIGC differs from human-generated
content. To explore these issues, we propose the following questions:

  * •

RQ1: How has the arrival of AIGC impacted the content creation _ecosystem_ ,
in terms of the volumes of content created and consumed over time, as well as
the levels of user engagement and content themes?

  * •

RQ2: How do individual AI and human content _creators_ differ, particularly in
terms of their productivity, profile information and interaction behavior?

  * •

RQ3: How does consumer engagement differ across AI and human generated
_content_?

To answer these questions, we focus on one exemplar platform: _Pixiv_ (pix,
2023c), an online community focusing on artwork. It serves as a dedicated
forum where artists can showcase their illustrations and receive feedback from
consumers. As of 2023, Pixiv hosts over 100 million artworks and receives more
than 1 billion page views per month (pix, 2023d). One key innovation of Pixiv
is its transparent mechanism for sharing AI-generated images. These images are
publicly tagged, making it easy for viewers (and researchers) to distinguish
between AI- and human-generated content. We argue that this makes Pixiv an
ideal lens through which to study the impact of AIGC. As far as we know, there
is currently no other image-based social media at scale that offers similar
features. To answer our RQs, we therefore gather a Pixiv dataset consisting of
15.2M artworks and over 937K creators. To the best of our knowledge, this is
the first in-depth empirical investigation into the characteristics of AIGC on
social platforms. Our findings include:

  1. (1)

The introduction of AIGC on Pixiv has resulted in a 50% increase in new
artworks, but no corresponding increase in the number of views or user
comments. (§4.1).

  2. (2)

After the introduction of AIGC, there has been a growth in the number of new
creators, but a 4.3% decrease in newly registered creators of human-generated
content. (§4.2).

  3. (3)

The themes and subjects of artworks have undergone significant changes, with a
decrease in diversity and a higher concentration of adult content and female
characters. (§4.3).

  4. (4)

Even though AI creators can generate artworks much faster, they do not upload
significantly more artworks than human creators (only 2 artworks more in the
50th percentile). (§5.1).

  5. (5)

AI creators are less communicative, and are more focused on using their posts
for monetization. (§5.2).

  6. (6)

Although AI-generated artworks are more popular in the middle and lower
percentiles, they cannot match the most popular human-generated artworks. This
indicates that AIGC plays an important role in content consumption, but is not
yet able to replace the top-level human creators. (§6.1).

  7. (7)

Whereas human-generated content consumption centers around the most popular
creators and themes (they receive more views/bookmarks per capita), the
popularity of AI-generated content is more uniformly distributed. (§6.2)

  8. (8)

Both human and AI creators are more likely to interact within their respective
groups, especially for human generated artworks, which receives 5x more
comments than AI generated ones from human creators on average. (§6.3)

##  2\. Primer on Pixiv

Overview. Pixiv is a Japanese website and online community for artists, which
emerged in Tokyo in 2007. In 2023, the platform hosts over 100 million
artistic submissions with a weekly increase of 0.2 million based on our
dataset (§3). It receives more than 1 billion page views per month (pix,
2023d). The primary objective of Pixiv is to furnish artists with a platform
to showcase their illustrations and receive feedback and user comments. Pixiv
excludes most forms of photography, and the majority of artworks on the
platform consist of _original_ artworks inspired by Japanese anime, and manga.
This makes it rather distinct from other platforms such as Instagram. The
website relies on a comprehensive tagging system to categorize the myriad of
artworks, serving as the foundation of the entire site. Pixiv also allows the
posting of explicit sexual content. Due to this, the site employs filters that
can be enabled by users.

User Profiles. A user’s profile page on Pixiv provides an overview of the
artist, including their self-reported nickname, birthday, gender, and
location. Additionally, users have the option to share a brief biography about
themselves. Furthermore, there is an additional profile section dedicated to
showcasing the artist’s workspace, offering insights into their creative
environment.

Artwork. Creators submit their images to Pixiv as _Artworks_. An artwork on
Pixiv can encompass multiple images. The creator of an artwork has the freedom
to provide a title and compose a caption to accompany the images. Each artwork
is accompanied by a set of up to 10 tags. Additionally, any user has the
ability to view the number of views and bookmarks an artwork has garnered.

Tags. Free-text tags play a significant role in Pixiv as they facilitate the
grouping of images based on shared themes and subjects. Each artwork is
limited to a maximum of ten tags, and the creator of the artwork can designate
certain tags as locked or unlocked. When a tag is unlocked, any user can
modify or remove it in a crowd-sourced fashion. Users are also allowed to add
additional tags, and there is a provision to report any tags that are deemed
unpleasant. Although they are lots of common tags that are widely used (pix,
2023f), users are free to create completely new tags.

Interaction. Users on Pixiv can follow other users, allowing them to keep
track of their artworks. Users can engage in direct communication by sending
each other messages and have the option to add each other as friends,
fostering a sense of community. Moreover, users can leave comments on images,
with a character limit of 140.

AIGC. Pixiv now permits the uploading of AI-generated images and implemented
new policies in late October 2022 (pix, 2022): (i) Artists are required to
explicitly indicate whether their submissions are human-generated or AI-
generated using a special toggle. This information is displayed when others
view the artwork. (ii) Users have the option to filter out all AI-generated
artworks if they prefer. This option is set to off by default. (iii) AI-
generated artwork and human-generated artwork are ranked separately. We argue
that this establishes Pixiv as a prominent platform for sharing AI-generated
works, making it an ideal case study.

Further, in May 2023, Pixiv updated its policies to enforce stricter rules on
AIGC, in response to the complains from human creators (pix, 2023e). These
changes include banning the use of AIGC to ”emulate” a specific human
creator’s style, and completely prohibiting AIGC on Fanbox, a subscription-
based platform linked to Pixiv that is a major source of monetization for
Pixiv creators (Fanbox, 2023).

##  3\. Dataset Collection

To answer our RQs, we gather publicly available data on Pixiv. See Appendix §A
for an ethics discussion.

Artwork Data. Each artwork has a unique ID that is chronologically sorted
based on its creation time. Our crawling process covers the range of artwork
IDs from 95180765 to 114914391, encompassing all the artworks created between
2022-01-01 and 2024-01-05. The dataset includes 15,203,948 artworks with
2,475,485 tagged as AI-generated (16.2%). We flag that before 2022-10, there
may be a small number of AI-generated artworks that were not explicitly
tagged. However, these are likely to be few in number as Stable Diffusion,
which is known to have created the boost in AIGC on Pixiv, was released on
2022-08-22 and received wide uptake in 2022-10 (with the release of Stable
Diffusion v1.5).

Creator Data. Every user is assigned a unique ID, and we gather user data for
the creators of the artworks in our dataset. Our dataset comprises a total of
937,130 users who have submitted at least one artwork between 2022-01-01 and
2024-01-05. The data of a user also encompasses myPixiv (i.e., friends) of the
user and users being followed by the user. However, the data does not include
the user’s followers as it is not publicly accessible.

Temporal Data. To track the changes in comments, views, and bookmarks over
time, we construct another dataset by performing daily crawls of data for
artworks generated in a week. The dataset consists of artworks within the ID
range of 108171000 to 108395275, which corresponds to a one-week period from
2023- 05-16 to 2023-05-24.

##  4\. RQ1: Ecosystem Temporal Analysis

Figure 1. Top to bottom: (a) Weekly count of new artworks; (b) Number of
comments generated under the artworks uploaded on each week; (c) Weekly
proportion of AI-generated artworks, and the proportion of views, bookmarks,
and comments received by AI-generated artworks uploaded in the same week. (d)
Weekly count of new creators; (e) Weekly count of active creators categorized
by the age of the account; (f) Weekly count of artworks categorized by the age
of the creator’s account. (g) Proportion of restricted (adult) artworks; (h)
Proportion of artworks with female-tags; (i) Proportion of artworks with male-
tags.

In this section, we explore the impact of AIGC on the overall Pixiv ecosystem
by inspecting the volumes of content creation, consumption and engagement both
before and after the introduction of AIGC (RQ1).

###  4.1. Impact on User Activity

We first investigate the overall user activity within the platform,
encompassing both content creation and consumption activity, as well as user
interaction activity.

Impact of AIGC on Creation Activity. Figure 1a illustrates the weekly count of
new artworks, both before and after the policy change allowing AIGC. This
represents the level of content creation activity on a weekly basis. We see
that the number of new artworks per week has been consistently increasing
since the introduction of generative AI until 2023-05, in contrast to the
stable patterns seen beforehand. Between 2022-10 and 2023-05, the weekly peak
rose from approximately 0.13 million to 0.2 million. This constitutes an
increase of over 50%. After that, the figure depicts a significant 10%
decline, attributed to the enforcement of more stringent regulations on AIGC
as detailed in §2. That said, the number of human-generated artworks also
decreased, which is a result of creators leaving Pixiv in protest against the
perceived ”abuse” of AI (lea, 2023). Overall, this suggests that the presence
of AIGC has significantly influenced the overall activity level of content
creation. Furthermore, the results in 2023-05 also indicate that without
proper policies, the presence of AIGC can impact the overall activity of
human-generated content creation.

Impact of AIGC on Consumption Activity. We next explore if the temporal
changes in consumption activity match the increase of creation activity caused
by AIGC. We rely on two metrics for measuring the consumption activity of an
artwork: the number of views and bookmarks. We first calculate the weekly
proportion of AI-generated artworks. Then, we determine the proportion of
views and bookmarks received by AI-generated uploaded in the same week. The
results are plotted in Figure 1c. We see that the percentage of views garnered
by AI-generated artworks is less than the percentage of AI-generated artworks,
as of 2023, by approximately 8%. However, this is not mirrored in the case of
bookmarks. Figure 1c highlights that AI-generated artworks contribute
significantly to the overall tally of bookmarks, with 32% of bookmarks
garnered by AI-generated artworks as of 2023-04.

The views reflect the general activity of consumption, while the bookmarks
signifies specific interest in consumption. With this in mind, the results
suggest that, although AI-generated artworks contribute a considerable
proportion (20%) of the general attention, this level of consumption cannot
match the level of creation. This might be attributed to the optional
mechanism provided to users, allowing them to filter AIGC content, as
discussed in §2. However, the proportion of bookmarks matches the count of
artworks, indicating that there is a segment of users who do demonstrate a
particular interest in AI-generated artworks.

Impact of AIGC on Interaction Activity. Figure 1b displays a time series of
the number of comments generated under the artworks uploaded each week. This
provides a measure of the level of interaction received for human vs. AI
generated content. It is evident that the number of comments remains
consistent both before and after the introduction of AIGC. Furthermore, AI-
generated artworks receive only a small fraction (12% as of 2023) of the total
comments as presented in Figures 1c. Thus, although people appear interested
in _viewing_ AIGC, they do not proceed to _interact_ with such users. The
exact reason is unclear, yet we conjecture that consumers may feel that human
creators are more connected to the artwork and therefore more worth
interacting with.

###  4.2. Impact on Creators

We next investigate the temporal impact of AIGC on the engagement of creators,
considering both the emergence of newly registered creators and the level of
activity among existing creators.

New Creators. Figure 1d illustrates a time series of the weekly count of new
creators (determined by the time the user uploads their first artwork). In the
figure, the classification of human-generated and AI-generated content is
based on the user’s artworks. If the user has uploaded both AI-generated and
human-generated artworks, they will be classified into Mix. The results
indicate an increasing trend in the number of new creators (rising from around
4K to 5K) following the introduction of AIGC. However, the period spanning
from 2022-01 to 2022-04 witnessed the inception of 69,326 new human creators,
whereas the timeframe from 2023-01 to 2023-04 saw a diminished count of 66,463
creators – marking a decrease of 4.3%. After the implementation of stricter
policies on AIGC in 2023-05, the number of new creators rebounded. This may
suggest that without strict rules on AIGC, the platform may become less
appealing to new creators of human-generated content.

Existing Creators Activity. We further explore the activity of existing
creators. Figure 1e displays the weekly count of active creators categorized
by the age of their accounts. The results indicate that the number of active
”new” creators (with an account age of less than 1 year) has increased
following the introduction of AIGC. However, the number of monthly active
”old” users remains stable. It is notable that there are approximately 23,000
monthly active creators with accounts that are at least 5 years old (33% of
the total monthly active creators). These suggest that the existing ”old”
creators continue to be as active as before.

Furthermore, Figure 1f illustrates the weekly count of artworks categorized by
users belonging to different ”age” groups (calculated as the age of the
account). Here, we observe a similar trend among older creators, who sustain
their previous levels of productivity. However, artworks originating from new
creators also display a notable surge, comprising nearly 50% of the total
artworks as in 2023-04. 56.2% of these artworks are AI-generated. This
observation potentially implies that while the ”old” creators retain their
activity, their overall contribution to the ecosystem diminishes comparatively
in the face of the burgeoning output from new AI creators.

###  4.3. Impact on Themes

We finally investigate the impact on the themes of the artworks. To identify
themes, we rely on the Pixiv’s tagging system. This mechanism groups images
based on shared themes and subjects, as discussed in §2 (see §B.1 for excluded
tags).

Diversity. As generative AI has the potential to enable individuals from all
backgrounds and with varying interests to create an artwork, we first look at
the diversity of themes within the artworks. We model content themes using the
tags allocated to each artwork and measure diversity using the Gini index. For
each month, we calculate the Gini index for tags on the number of artworks
associated with tags. We observe an increase in the Gini index following the
introduction of AIGC: it was 0.85 in 2022-10, increasing to 0.90 by 2023-04
and 0.88 in 2023-12. This outcome implies that the diversity of artwork themes
has _declined_ since the introduction of AIGC. To investigate this, we
calculate the monthly ranking of tags (listed in Table 2 in the appendix).
Through this ranking, we observe a notable increasing trend in tags related to
R-18 content and female characters, while a decreasing trend in tags related
to male characters. We therefore explore this trend in the following
paragraphs.

Restricted Content. Figure 1g illustrates the proportion of restricted (adult)
artworks. The proportion of restricted artworks saw a significant increase
after the introduction of AIGC, rising from approximately 20% per month to
over 30%. As of 2023-12, over half of all R-18 artworks are generated by AI,
confirming that this increase can be attributed to AIGC. Overall, these
findings indicate that the presence of AIGC has contributed to a substantial
increase in adult content on Pixiv.

Male vs. Female. In addition to the increase in restricted content, we also
observe a new skew towards female characters. To investigate this trend, we
group tags based on how frequent two tags are under the same artwork. We then
manually label them as pertaining to male or female characters (see appendix
§B.3 for full details). Using this method, we obtain 439 representative tags
for female characters (female-tags) and 98 representative tags for male
characters (male-tags). Figures 1h and 1i illustrate the proportion of
artworks with female-tags and male-tags, respectively.

Figures 1h shows a significant increase in the percentage of artworks
featuring female-tags, rising from 41% to 53%. Furthermore, a considerable
proportion of these artworks are AI-generated, and this proportion has been
increasing. As of 2023-04, AI-generated artworks make up 25% of the total,
nearly half of all female-tagged artworks. However, for male-tagged artworks
displayed in Figure 1i, the percentage keeps stable, with only a small
fraction being AI-generated. These results suggest that after the introduction
of AIGC, the subject matter of artworks on Pixiv has shifted towards female
characters, traditionally considered male-oriented (男性向け) themes.

Diversity Revisit. To further explore the relationship between the previously
observed decrease of diversity and R-18 and female characters content, we re-
calculate the monthly Gini index, excluding both themes respectively, as shown
in Figure 2. We observe that by 2023-04, without R-18 artworks, the Gini index
only increases from approximately 0.83 to 0.86, which is smaller than the
overall increase (0.85 to 0.9). Furthermore, when excluding artworks with
female tags, the Gini index remains around 0.79 and does not show an increase.
This finding confirms that the decline in diversity is associated with the
rise in adult content and female characters in AIGC.

Figure 2. Monthly Gini index for tags on the number of artworks associated
with the tag. inline,color=whiteinline,color=whitetodo:
inline,color=whiteTakehomes: (i) AIGC on Pixiv has led to 50% more new
artworks, but no corresponding increase in views or comments. (ii) Without
strict policies, AIGC can impact the engagement of creators, decreasing the
number of newly registered human-generated content creators by 4.3%. (iii)
Artwork themes have changed with less diversity and more adult content and
female characters.

##  5\. RQ2: Per-Creator Analysis

Our temporal analysis in RQ1 (§4) confirms that AIGC has had a significant
impact on Pixiv. In order to gain a deeper understanding of AIGC, we now
explore the difference between the _creators_ of AIGC and human-generated
content (RQ2). For our analysis, we utilize data from our dataset starting
from 2022-11-01, as this portion of the data is specifically categorized as
either AI-generated or human-generated.

###  5.1. Creator Productivity

AIGC makes it possible to produce large volumes of content in a short period.
Therefore, we first investigate the difference in productivity of AI-generated
and human-generated creators.

Artwork Creation Time. We first investigate the time taken by the creator to
complete an artwork. We hypothesize that AI creators may produce content much
faster. To estimate this, we calculate the interval (in days) between the
upload times of two consecutive artworks by the same creator. The CDF is
displayed in Figure 3a. It is evident that the interval between the upload
times of AI-generated artworks are significantly smaller than those of human-
generated artworks. 55% of AI-generated artworks are uploaded on the same day
as the creator’s previous work, whereas for human-generated artworks, this is
only 20%. These findings suggest that the creation of AI-generated artworks
takes significantly less time, confirming that AI creators are indeed more
”efficient”.

Number of Artworks. We next proceed to investigate the number of artworks. We
hypothesize that AI creators may produce much more artworks as we previously
find that AI creators are more ”efficient”. Figure 3a plots the CDF of the
number of artworks submitted by each creator. It is evident that AI creators
have a larger number of artworks compared to human creators across all
percentiles, albeit not significantly higher (as compared to the significantly
higher ”efficiency” of AI creators). At the 50th percentile, human creators
have 4 artworks, while AI creators have 5 artworks. In order to gain a deeper
understanding of the tail end of Figure 3b, Figure 3c plots the distribution
of artwork uploaded by creators. We observe that the overall distribution of
AI and human creators is similar, with a minority of highly productive
creators generating the majority of artworks. Additionally, the figure shows
that the contribution of the top AI and human creators is comparable, although
the most productive AI creators contribute slightly more artworks in
proportion (17% vs. 13%). Thus, in contrast to our previous observations,
there is not a significant difference between human and AI creators in the
number of artworks produced, either regarding the overall distribution or per-
creator.

Upload Patterns. The previous two paragraphs show that, although AI creators
produce works faster (Figure 3a), the overall number of artworks produced by
AI creators is not significantly higher than that of a human creator (Figure
3b). To better explain this, Figure 3d plots the count of active days of a
creator vs. the count of their artworks. We see that AI creators generally
require fewer active days to produce the same number of artworks. On average,
an AI creator uploads 2.2 artworks per active day while this is 1.2 for a
human creator. This is because many AI contributors upload a substantial batch
of artworks at once, followed by periods of inactivity that span several days.
This could be attributed to either AI creators (i) generating numerous
artworks in a single session; or (ii) continuously producing images, but only
uploading the finest pieces in bulk. The exact reason is unclear, but this
process does set AI creators apart from their human counterparts.

Figure 3. (a) CDF of the difference between the upload times of two
consecutive artworks by the same creator; (b) CDF of the number of artworks of
each creator; (c) Distribution of artwork uploaded by creators; (d) Count of
active days of a creator vs. the count of artworks created by the same
creator. Figure 4. (a) Jobs of creators; (b) Percentage of artworks that
include a link to an external platform.

###  5.2. Creator Profiles & Activities

We now examine the distinctions between the profiles, as well as the
communication and monetization activities of AI creators and human creators.
Note, the gender and job information are all self-reported, which may not be
entirely reliable.

Gender. Approximately 58% of human creators and 55% of AI creators do not
provide their gender information in our dataset. Among the users who do
disclose their gender information, approximately 50% of human creators
identify themselves as male, and the other 50% identify as female. The AI
creators stand in stark contrast, with a male:female ratio of 80-20. This
observation may perhaps help explain the skew towards female characters
discussed in §4.3.

Job. Figure 4a presents the distribution of jobs among creators. The most
significant observation is that around 14% of AI creators label themselves as
working in the IT industry, which is notably higher than human creators (5%).
Moreover, the percentage of AI creators working as engineers is also slightly
higher compared to human creators with an engineering job (4.8% vs. 3.8%).
This result is intuitive since AI creation requires certain skills that align
well with individuals having IT backgrounds.

Communications. We start by examining the communication behavior of the
creators, as this is a crucial factor in fostering an active community. There
are two broad ways that creators can communicate with their audience (beyond
the image itself). These are by including text in the user profile, or by
attaching a caption to the artwork. We therefore first focus on the presence
of captions. We find that 42% of AI-generated artworks lack captions, which is
much higher compared to human-generated artworks (29%). Additionally, 41% of
AI creators do not have captions on their profile pages, while human creators
only have 22% without captions. These findings indicate that AI creators might
be less inclined to communicate personally with their audience.

Monetization. We also notice that some users submit their artworks as samples
or advertisements to promote their paid subscription on other platforms such
as Fanbox, or to attract more followers on social media platforms such as
Twitter. Indeed, we find that 57% of human creators and 28% of AI creators
list a Twitter handle in their profile bio. To investigate this further, we
extract all the external URLs in the captions of the artworks. We identify
9.72 million URLs in total, covering over 18.4k domains.

Figure 4b illustrates the percentage of artwork captions that include a link
to the top 10 external domains. We find that 9% of AI-generated artworks
include a link to Fanbox, surpassing the 5% found in human-generated artworks.
Moreover, before 2023-05 (when Fanbox started banning AIGC), the numbers are
16.4% vs. 5%. Additionally, we also find that a higher percentage of AI-
generated artworks contain links to other monetization platforms (e.g.,
Patreon and booth) when compared to human-generated artworks. These findings
suggest that AI creators are more inclined to promote their paid
subscriptions. This also aligns with our results in §4.1: the prohibition of
AIGC in Fanbox led to a significant decrease in the number of AI-generated
artworks.

inline,color=whiteinline,color=whitetodo: inline,color=whiteTakehomes: (i) AI
creators generate artworks faster but do not upload significantly more
artworks than human creators. (ii) There are significant differences in gender
ratio and jobs between AI creators and human creators. (iii) AI creators are
less communicative and make greater use of monetization services.

##  6\. RQ3: Per-Content Analysis

Figure 5. (a) Count of views and bookmarks in the 50th, 75th, 90th, and 99th
percentiles in relation to the age of the artwork; (b) Boxplot of the views
and bookmarks per artwork, the whiskers are 5th and 95th percentiles.

Through our investigation on RQ2 (§5), we discovered a contrast between the
producers of AIGC and human-generated content. We are next curious if there
are also difference in the consumption of individual artworks. Therefore, we
next investigate the levels of engagement received for AI vs. human generated
content (RQ3).

###  6.1. Overall Consumption Activity

We begin by contrasting the overall consumption volumes for AI-generated vs.
human-generated individual artworks. We rely on two metrics for measuring the
consumption activity of an artwork: the number of views and bookmarks. For
this, we utilize the temporal data covering views and bookmarks outlined in
§3. Figure 5a displays the count of views and bookmarks in the 50th, 75th,
90th, and 99th percentiles in relation to the age of the artwork.

Views & Bookmarks.  We see that AI-generated artworks tend to have more views
in the 50th and 75th percentile, but are surpassed by human-generated artworks
in the 90th percentile, and lag even further behind in the 99th percentile.
Specifically, in the 90th percentile, human-generated artworks have 33% more
views, and in the 99th percentile, human-generated artworks have a remarkable
200% more views. The bookmarking outcomes for AI-generated artworks exhibit a
more favorable trend. AI artworks tend to accumulate more bookmarks within the
50th, 75th, and 90th percentiles. However, human-generated artworks surpass
AI-generated ones by a factor of 1x in the 99th percentile. The results
suggest that, although AI-generated artworks are not yet able to compete with
top-tier human-generated artworks, lower- and mid-popularity AI artworks seem
to surpass human-generated material. Thus, whereas there is no evidence that
top-tier human creators are being crowded-out, there may be a more detrimental
impact on amateur creators.

Distribution Differences. The above suggests that human artworks may
experience a more skewed popularity distribution. To confirm this, we measure
the difference in the popularity distributions of human-generated vs. AI-
generated content. Here, we calculate the Gini index for the number of views
and bookmarks, respectively, for all artworks in our temporal data. For each
artwork, we extract their view/bookmark count 10 weeks (70 days) after they
were initially uploaded. The Gini index for human-generated artworks is 0.82
for views and 0.85 for bookmarks, whereas for AI-generated artworks, it is
0.64 for views and 0.69 for bookmarks. Confirming our intuition, AI-generated
artworks _do_ have a substantially more uniform popularity distribution. We
suspect this is because the quality difference among human artworks is far
greater than that among AI-generated content.

###  6.2. Popular Themes & Creators of Consumption

We are next curious about the distribution of views across creators and themes
(using tags, see §B.1 for excluded tags). To gain deeper insights into this,
we zoom in to one single day in our temporal data. Figure 5b present a boxplot
of the number of views and bookmarks per artwork. For each artwork, we take
their counts 10 weeks (70 days) after they were uploaded. We group the
artworks by three categories respectively: (i)  _None_ presents the
distribution of the number of views/bookmarks across all artworks, i.e.,
grouped by nothing; (ii)  _Tag_ presents the distribution per-tag, i.e., each
data point represents the average number of views/bookmarks for all artworks
associated with that tag; (iii)  _Creator_ presents the distribution of
views/bookmarks per creator.

We see that the distribution of human-generated artworks is more varied than
that of AI-generated works, with similar medians but significant differences
in the 95th percentile and higher outliers. This suggests that the consumption
of human-generated content tends to focus on the most popular artworks,
creators, and themes. While for AI-generated content, the consumption is more
evenly distributed. One possible explanation is that the quality of AI-
generated artworks is more consistent, allowing even novice creators to
produce reasonable works. In contrast, for human creators, it is natural that
some artworks produced by talented artists are far superior to most other
artworks and, therefore, attract a larger audience. However, this fails to
explain the distribution of tags. Another possible explanation is that
consumers of human-generated artworks tend to have specific targets, such as
themes or creators, leading to a centralized distribution around popular tags
and creators. In contrast, consumers of AI-generated artworks may be more
likely to browse randomly.

###  6.3. Who Comments?

We now look at the comments left on artworks. This reflect the interaction
activity of creators and consumers. We are curious about whether there is a
difference between those who comment on AI- vs. human-generated artworks. To
investigate this, ”commenters” are classified into six categories: AI
creators, human creators, mixed creators, unspecified creators, not creator,
and self if the comment is from the creator themselves. Figure 6a displays the
percentage of comments under AI- and human-generated artworks from different
user categories. Most comments (more then 80%) are from non-creators or the
creators themselves. We also observe another interesting trend in which
human/AI creators have more comments under human-/AI- generated artworks,
respectively. To measure this, for each user category $C$, we define ratio:

$R=\begin{cases}{NC\\_H^{C}}\ /\ {NC\\_AI^{C}}&\text{if }{NC\\_H^{C}}\ /\
{NC\\_AI^{C}}>1\\\ -({NC\\_H^{C}}\ /\
{NC\\_AI^{C}})^{-1}&\text{otherwise}\end{cases}$

where $NC\\_H^{C}$ is the number of comments on human-generated artworks from
$C$, and $NC\\_AI^{C}$ is the number of comments on AI artworks from category
$C$. A larger value indicates that users in this category have more comments
under human-generated artworks. For example, $R=2$ for the category ($C$) of
all-users, indicates that from all-users, human-generated artworks receive
twice as many comments compared to AI-generated artworks.

The results are plotted in Figure 6b. Indeed, we see that from all-users, the
ratio is 8, indicating that human-generated artworks receive 8x as many
comments as AI-generated artworks. Interesting, we also see homophily: From
human-creators, human-generated artworks receive 15x more comments than AI-
generated artworks. In contrast, from AI-creators, human-generated artworks
receive just 0.4x more comments than AI-generated artworks. These trends,
however, are partly driven by the fact that there are more human-generated
artworks than AI ones (1.8x more in our dataset). Thus, we also normalize this
ratio by the number of artworks. Specifically, we calculate normalizer
$N={NA\\_H}\ /\ {NA\\_AI}$, where $NA\\_H$ is the number of human-generated
artworks and $NA\\_AI$ is the number of AI-generated artworks. Then we
calculate the ratio $R^{\prime}={NC\\_H^{C}}\ /\ {NC\\_AI^{C}}\ /\ N$, then
the normalized ratio $R_{n}$:

$R_{n}=\begin{cases}R^{\prime}&\text{if }R^{\prime}>1\\\
-(R^{\prime})^{-1}&\text{otherwise}\end{cases}$

This offers the ratio of the average number of comments received per artwork.
For example, a normalized ratio of 2 from the category all-users, indicates
that from all-users, human-generated artworks receive twice as many comments
on average per artwork than AI-generated artworks do. The normalized ratios
are also depicted in Figure 6b. We see that, when normalized by the number of
artworks, AI-generated artworks actually obtain twice as many comments from
AI-creators, compared to human-generated artworks. These results confirm that
human creators are more likely to interact with human-generated artworks, and
AI creators are more likely to interact with AI-generated artworks, suggesting
a notable difference in the interaction between creator groups under artworks.

inline,color=whiteinline,color=whitetodo: inline,color=whiteTakehomes: (i) AI-
generated artworks are more popular in the middle and lower percentiles, but
cannot match the top human-generated ones. (ii) Human-generated content
consumption centers around the most popular creators and themes, while for AI-
generated content it is more evenly distributed. (iii) Both human and AI
creators are more likely to interact within their respective groups.

Figure 6. (a) Category of the users who left the comments; (b) Ratio of the
number of comments under human-generated and AI-generated artworks from
different user categories.

##  7\. Discussion

Through our investigation on RQ1 (§4), we confirmed that AIGC has had a
significant impact on the overall ecosystem, particularly in terms of content
creation volumes. Further, our analysis of RQ2 (§5) and RQ3 (§6) reveals
notable differences between the creators and consumers of AIGC compared to
human-generated content. These findings offer key insights into the role of
AIGC within online social media. Here, we discuss important implications.

Abuse of Generative AI. There are concerns that with the advent of generative
AI, certain users may generate an abundance of low quality content and share
it via social platforms. However, our study does not reveal such abuse.
Although the allowance of AIGC on Pixiv has led to 50% more artworks (§4.1),
this is arguably within a reasonable range. Furthermore, we find that the
number of bookmarks received by AI-generated artworks can actually match this
increase (§4.1), confirming these AI artworks are not spam. Further, we find
that AI creators are more ”efficient” (creating artworks faster), but the
number of artworks produced by an AI creator is not significantly higher than
that of a human creator (§5.1). This is because they tend to follow a specific
pattern, where they upload in batches. This could be due to their inclination
to produce multiple images in a single sitting or creating a large number of
pieces and then sharing the best ones in batches. These findings could be
useful for platforms looking to develop policies and algorithms to better
manage AI-generated content and their creators.

Impact on Human Creators. There have been criticisms within the artistic
communities regarding the influence of AIGC on the engagement and activity of
creators of human-generated content (pix, 2023a, b). Indeed, our findings
reveal a 4.3% decline in the number of newly registered human creators
following the introduction of AIGC, lending support to this view (§4.2).
Additionally, although our research in §4.2 indicates that the presence of
AIGC has had a limited impact on active existing users and the quantity of new
human-generated artworks, we also observe a significant increase in the number
of artworks originating from new creators. These make up almost 50% of all
artworks as of 2023-04, and most of these (56.2%) are generated by AI.
Moreover, we find that AI artworks in the mid- and lower-percentiles seem to
surpass human-generated ones (§6.1). This suggests that while the engagement
levels of ”old” creators remain the same, their significance in the community
may be diminishing compared to the massive output of new (AI) creators.
Although our results show that AI cannot compete with top-tier human creators,
amateur human creators may suffer.

Impact on Content Diversity. The impact of AIGC on the themes of the content
is significant. We observe a decrease in the diversity of themes, as evidenced
by the Gini index. This decrease can be attributed to a higher concentration
of adult content (50% increase in proportion) and female characters (30%
increase in proportion). Overall, our results suggest that the introduction of
AIGC can profoundly alter the themes of the platform, thereby potentially
influencing the culture of the community. This is perhaps one of the more
worrying trends, and we argue that it is crucial for online social platforms
to approach this matter with careful consideration.

User Communication & Interaction. Our analysis of the creator profile reveals
differences between AI and human creators concerning gender ratio and
job(§5.2). Further, §5.2 highlights that AI creators tend to be less
communicative and are more motivated by monetization. With time, this may
impact the demographics and culture of the platform. The interaction on human-
and AI-generated content also differs. Only a small proportion of comments are
under AI-generated artworks (§4.1), suggesting that people might be more
inspired to reach out for human created content. We argue that these findings
have key implications for community formation. Online social communities
should carefully consider how to incorporate AI creators, who may not actively
engage in communication within the existing community.

Content Consumption Behaviour. Our results reveal that AI-generated artworks
are more popular in the lower percentiles (under 90th percentile), while
human-generated artworks are more popular in the higher percentiles (§6.1).
Additionally, AI-generated artworks get more bookmarks per view, indicating
consumers may have specific interests in them. In contrast, consumption of
human-generated content focuses on popular creators and themes, while for AI-
generated content it is more uniformly distributed (§6.2). This may be due to
the consistency in quality of AI-generated artworks, or simply because
consumers of human-generated artworks tend to have specific targets of
creators and tags. Either way, these findings highlight a significant
difference in content consumption behavior between human-generated and AI-
generated artworks. This sheds important light on how people perceive AIGC. We
also posit that this may offer useful insights into the design of
recommendation algorithms, which may benefit from using the use of AI tags as
an explicit feature.

##  8\. Related Work

Image-based Social Media Platforms. Numerous studies have measured image-based
social platforms. Among these platforms, Instagram has receives the most
attention (Rejeb et al., 2022). Other platforms that have been studied include
Flickr (van Zwol, 2007; Mislove et al., 2008; Negoescu and Gatica-Perez,
2008), Pinterest (Gilbert et al., 2013; Ottoni et al., 2021; Lo et al., 2016),
and Tumblr (Chang et al., 2014; Hillman et al., 2014). These platforms are
generally more focused on photographs and have a broader purpose.
Additionally, there have been studies on platforms for artists, such as
Behance (Kim, 2017), Dribbble (Hemsley and Kelly, 2019), ArtStation (Wijaya et
al., 2021), and deviantArt (Salah et al., 2012; Buter et al., 2011; Wijaya et
al., 2021). These platforms cater more to designers and their need for design
elements. Thus, their art styles and communities are vastly different from
that of Pixiv. While there have also been studies conducted on Pixiv (Lee and
Lee, 2019; Watabe and Abe, 2016), they are qualitative and questionnaire-
based. To the best of our knowledge, this is the first large-scale empirical
study of Pixiv.

AIGC on Social Media Platforms. Several recent studies have delved into the
repercussions of AIGC on social media platforms. Haque et al. investigated the
attitudes of ChatGPT users using Twitter data and found that early adopters
expressed positive sentiments, especially in areas such as entertainment and
creativity (Haque et al., 2022). This correlation is consistent with our
findings, which show an increasing number of AI-generated artworks on Pixiv
and a high bookmark rate for them. Chen and Zou compiled a dataset of AI-
generated images on Twitter, revealing a significant amount of NSFW,
pornographic, and nude content (Chen and Zou, 2023). Another study analyzed
people’s perceptions of ChatGPT using content from Twitter and Reddit,
highlighting concerns about potentially harmful or NSFW content (Feng et al.,
2023). These findings align with our observations of the rise in R-18 content
on Pixiv. Several other recent studies have reported similar results
(Taecharungroj, 2023; KORKMAZ et al., 2023; Miyazaki, Kunihiro et al., 2024).
However, these investigations focus on individuals’ attitudes and opinions
toward AIGC, while our paper directly measures the impact of AIGC and its
unique characteristics at scale.

Other studies have examined the use of AI-generated images in social media
(Mink et al., 2022; Khoo et al., 2022; Gamage et al., 2022; Lu et al., 2023).
They come from a security perspective though, e.g., fake content and phishing.
Additionally, there have been studies exploring the use of AIGC for journalism
(Breuer and Jonsson, 2023) and advertisements on social media (Du et al.,
2023). Our research is distinct from previous studies since we measure the
impact and characteristics of AIGC on social media based using a large-scale
dataset. To the best of our knowledge, we are the first to undertake such a
study.

##  9\. Conclusion

This paper has presented a comprehensive study of the impact of AIGC on Pixiv,
a major online community for sharing artworks. Our findings provide valuable
insights into the effects of AIGC on online social media. We hope that our
findings can support platforms in taking appropriate measures to address the
challenges of AIGC and enhance their policy formulation, community
construction, and algorithmic design. In the future, we hope to expand our
analysis to other platforms, such as Twitter and Instagram.

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##  Appendix A Ethics

This study is approved by the institutional review board (IRB). All the data
used in this paper is publicly available to any ordinary Pixiv user. Personal
information may be present in user profiles, but it is important to note that
it is the user who voluntarily provided and chose to make it public. We do not
analyze any specified user nor do we attempt to link these Pixiv accounts to
real personal identities. Instead, we aggregate the data to gain a high-level
understanding.

Furthermore, it is worth noting that our analysis does not involve using
artwork (images) created by Pixiv users for either data analysis or machine
learning model training. Instead, we rely on metadata such as tags, views, and
bookmarks. Again, we emphasize that we do not perform analysis on any specific
artwork, and only aggregate the data to gain an overall understanding. This
approach ensures that we do not prejudice the interests of creators.

##  Appendix B Tags

###  B.1. Excluded Tags

When analyzing tags, we exclude two types of tags that contain meta-
information, which can affect our analysis. The first type is AI-generated
related tags, and the excluded tags are listed in Table 1. The second type
includes tags that explicitly display the number of bookmarks, such as
”10000users入り”, indicating that the tagged artwork has more than 10000
bookmarks. Therefore, we exclude all tags with ”users入り”. In Section §6.2, our
focus is on popular themes. Recall, tags are free-text. To eliminate the
impact of tags that are seldom used, we only consider tags that are associated
with at least 0.01% of the AI-generated or human-generated artworks.

AI | AI-generated | AIart | AIartist
---|---|---|---
AIartwork | AIdrawing | AIアート | AIイラスト
AIグラビア | AI作成 | AI作画 | AI生成
AI画像生成 | AI絵 | AI絵描き | AI绘画
Ai | AiArt | Aiイラスト | Ai绘画
NOVELAI | NovelAI | NovelAIDiffusion | NovelAIdiffusion
NovelAi | NovelAiDiffusion | StableDiffusion | WaifuDiffusion
discodiffusion | novelAI | novelaidiffusion | stable-diffusion
stable_diffusion | stablediffusion | waifudiffusion |
画像生成AI | 手作業加筆修正済みAI絵 |
Table 1. Excluded AI-related Tags

###  B.2. Tag Rankings

We calculate monthly rankings for tags. Table 2 displays the top 90 tags for
2022-04, 2022-07, 2022-10, 2023-01, and 2023-04. The numbers in parentheses
indicate the change of the rank. Caution: The table contains explicit sexual
words. For transparency, we decide to present the tags in their original form.

###  B.3. Tag Classification

  1. (1)

Choose two sets of ground truth tags, labeled as $S_{1}$ and $S_{2}$.

  2. (2)

Calculate the percentage of artworks that have tags in $S_{1}$ and $S_{2}$,
respectively, out of all artworks. These percentages are denoted as $P_{1}$
and $P_{2}$.

  3. (3)

For each individual tag $T$, calculate the percentage of artworks that have
tags in $S_{1}$ and $S_{2}$, respectively, out of all artworks that have $T$.
These percentages are denoted as $P_{T1}$ and $P_{T2}$.

  4. (4)

Calculate the ratios $R_{T1}=P_{T1}/P_{1}$ and $R_{T2}=P_{T2}/P_{2}$.

Intuitively, if $R_{T1}>1$, this means that tags in $S_{1}$ appear more
frequently in artworks with tag $T$ than average, indicating a higher
likelihood of similarity in type. The same applies for $R_{T2}$. Now we
classify tags into male-tags and female-tags. We choose

  * •

$S_{male}=$ {’男の子’, ’腐向け’, ’少年’, ’boy’, ’ショタ’}

  * •

$S_{female}=$ {’女の子’, ’ロリ’, ’百合’, ’少女’, ’girl’}

We then calculate $R_{Tmale}$ and $R_{Tfemale}$ for each tag in the top 1000
tags. For a tag $T$, if $R_{Tmale}>1$ and $R_{Tfemale}<1$, we classify it into
male-tags. If $R_{Tmale}<1$ and $R_{Tfemale}>1$, we classify it into female-
tags. The result is plotted in Figure 7.

Figure 7. $R_{Tmale}$ vs. $R_{Tfemale}$ for the top 1000 tags. | 2022-04 | 2022-07 | 2022-10 | 2023-01 | 2023-04
---|---|---|---|---|---
Rank |  |  |  |  |
1 | R-18 | R-18 (=) | R-18 (=) | R-18 (=) | R-18 (=)
2 | オリジナル | オリジナル (=) | オリジナル (=) | 女の子 (+1) | 女の子 (=)
3 | 女の子 | 女の子 (=) | 女の子 (=) | オリジナル (-1) | オリジナル (=)
4 | 漫画 | 漫画 (=) | 漫画 (=) | おっぱい (+1) | 巨乳 (+1)
5 | おっぱい | おっぱい (=) | おっぱい (=) | 巨乳 (+1) | おっぱい (-1)
6 | イラスト | イラスト (=) | 巨乳 (+2) | 漫画 (-2) | 漫画 (=)
7 | 創作 | 原神 (+2) | 原神 (=) | イラスト (+1) | 少女 (+5)
8 | 巨乳 | 巨乳 (=) | イラスト (-2) | 原神 (-1) | 原神 (=)
9 | 原神 | 水着 (+27) | ハロウィン (new) | 創作 (+2) | girl (+5)
10 | アナログ | 創作 (-3) | ロリ (+13) | ロリ (=) | ロリ (=)
11 | バーチャルYouTuber | バーチャルYouTuber (=) | 創作 (-1) | GenshinImpact (+7) | イラスト (-4)
12 | ウマ娘プリティーダービー | ホロライブ (+1) | 少女 (+10) | 少女 (=) | 美少女 (+26)
13 | ホロライブ | アナログ (-3) | 東方 (+2) | バニーガール (+74) | ブルーアーカイブ (+3)
14 | ポケモン | ウマ娘プリティーダービー (-2) | アナログ (-1) | girl (+5) | GenshinImpact (-3)
15 | 東方 | 東方 (=) | バーチャルYouTuber (-4) | ポケモン (+6) | 創作 (-6)
16 | ウマ娘 | VTuber (+2) | ブルーアーカイブ (+9) | ブルーアーカイブ (=) | 魅惑の谷間 (+6)
17 | fanart | GenshinImpact (+7) | ホロライブ (-5) | バーチャルYouTuber (-2) | 魅惑のふともも (+14)
18 | VTuber | ポケモン (-4) | GenshinImpact (-1) | 東方 (-5) | 水着 (+5)
19 | オリキャラ | オリキャラ (=) | girl (+5) | ホロライブ (-2) | 爆乳 (+1)
20 | 少女 | fanart (-3) | ウマ娘プリティーダービー (-6) | 爆乳 (+2) | 制服 (+22)
21 | ロリ | ウマ娘 (-5) | ポケモン (-3) | VTuber (+2) | 極上の乳 (+16)
22 | girl | 少女 (-2) | 爆乳 (+8) | 魅惑の谷間 (+55) | ブルアカ (+17)
23 | R-18G | ロリ (-2) | VTuber (-7) | 水着 (+4) | バーチャルYouTuber (-6)
24 | GenshinImpact | girl (-2) | オリキャラ (-5) | アナログ (-10) | ホロライブ (-5)
25 | illustration | ブルーアーカイブ (+4) | R-18G (+1) | オリキャラ (-1) | R-18G (+1)
26 | コイカツ | R-18G (-3) | fanart (-6) | R-18G (-1) | VTuber (-5)
27 | 3DCG | コイカツ (-1) | 水着 (-18) | ぼっち・ざ・ろっく! (new) | 東方 (-9)
28 | ケモノ | illustration (-3) | ウマ娘 (-7) | ウマ娘プリティーダービー (-8) | ウマ娘プリティーダービー (=)
29 | ブルーアーカイブ | 3DCG (-2) | illustration (-1) | fanart (-3) | ポケモン (-14)
30 | original | 爆乳 (+1) | コイカツ (-3) | 東方Project (+1) | 3DCG (+5)
31 | 爆乳 | ケモノ (-3) | 東方Project (+2) | 魅惑のふともも (+44) | アナログ (-7)
32 | 落書き | ふともも (+118) | ケモノ (-1) | ウマ娘 (-4) | 金髪 (+26)
33 | 東方Project | 東方Project (=) | 3DCG (-4) | illustration (-4) | ウマ娘 (-1)
34 | うごイラ | にじさんじ (+6) | Fate/GrandOrder (+1) | コイカツ (-4) | fanart (-5)
35 | 腐向け | Fate/GrandOrder (+4) | FGO (+2) | 3DCG (-2) | オリキャラ (-10)
36 | 水着 | 落書き (-4) | OC (+3) | ケモノ (-4) | 極上の女体 (+47)
37 | OC | FGO (+1) | loli (+48) | 極上の乳 (+22) | 中出し (+6)
38 | FGO | original (-8) | 百合 (+13) | 美少女 (+26) | 女子高生 (+22)
39 | Fate/GrandOrder | OC (-2) | 拘束 (+9) | ブルアカ (+1) | BlueArchive (+66)
40 | にじさんじ | hololive (+2) | ブルアカ (+15) | Fate/GrandOrder (-6) | illustration (-7)
41 | 男の子 | うごイラ (-7) | うごイラ (=) | 百合 (-3) | loli (+13)
42 | hololive | 腐向け (-7) | hololive (-2) | 制服 (+12) | 黒髪 (+31)
43 | 拘束 | 初音ミク (+8) | original (-5) | 中出し (+3) | sexy (+55)
44 | 艦これ | anime (+3) | チェンソーマン (+78) | FGO (-9) | anime (+9)
45 | 明日方舟 | ビキニ (+133) | anime (-1) | hololive (-3) | 東方Project (-15)
46 | アークナイツ | 明日方舟 (-1) | 中出し (+21) | 男の子 (+2) | ケモノ (-10)
47 | anime | 男の子 (-6) | 初音ミク (-4) | original (-4) | ふともも (+32)
48 | furry | 拘束 (-5) | 男の子 (-1) | OC (-12) | 百合 (-7)
49 | SPY×FAMILY | 艦これ (-5) | 落書き (-13) | 拘束 (-10) | 拘束 (=)
50 | 制服 | アークナイツ (-4) | 腐向け (-8) | うさぎ (new) | hololive (-5)
51 | 初音ミク | 百合 (+1) | 艦これ (-2) | うごイラ (-10) | ビキニ (+38)
52 | 百合 | furry (-4) | pixivファンタジアSOZ (new) | ポケモンSV (new) | original (-5)
53 | ふたなり | うちの子 (+3) | 水星の魔女 (new) | anime (-8) | Fate/GrandOrder (-13)
54 |  アイドルマスター シンデレラガールズ | VOCALOID (+13) | 制服 (+2) | loli (-17) | コイカツ (-20)
55 | art | ブルアカ (+33) | furry (-3) | 年賀状 (new) | JK (+46)
56 | うちの子 | 制服 (-6) | 風景 (+2) | furry (-1) | cute (+18)
57 | 風景 | 裸足 (+3) | 明日方舟 (-11) | 明日方舟 (=) | FGO (-13)
58 | ショタ | 風景 (-1) | 猫耳 (+45) | 金髪 (+20) | Realistic (new)
59 | 中出し | Vtuber (+4) | 極上の乳 (+19) | むちむち (+8) | バニーガール (-46)
60 | 裸足 | ふたなり (-7) | ショタ (+6) | 女子高生 (+36) | 男の子 (-14)
61 | 艦隊これくしょん | おへそ (+32) | ふたなり (-1) | 全裸 (+2) | pixiv今日のお題 (+32)
62 | commission | アズールレーン (+6) | アークナイツ (-12) | ショタ (-2) | 露出 (+20)
63 | Vtuber | art (-8) | 全裸 (+12) | 艦これ (-12) | うごイラ (-12)
64 | 原创 |  アイドルマスター シンデレラガールズ (-10) | 美少女 (+47) | アークナイツ (-2) | 全裸 (-3)
65 | nsfw | 夏 (new) | うちの子 (-12) | ファンタジー (+20) | 猫耳 (+20)
66 | コイカツ! | ショタ (-8) | にじさんじ (-32) | 落書き (-17) | 風景 (+10)
67 | VOCALOID | 中出し (-8) | むちむち (+24) | うちの子 (-2) | ファンタジー (-2)
68 | アズールレーン | 艦隊これくしょん (-7) | art (-5) | 腐向け (-18) | 貧乳 (+9)
69 | 3D | commission (-7) |  アイドルマスター シンデレラガールズ (-5) | 後藤ひとり (new) | むちむち (-10)
70 | 桜 | プロセカ (+7) | ONEPIECE (+83) | にじさんじ (-4) | 明日方舟 (-13)
71 | スパイファミリー | 3D (-2) | VOCALOID (-17) | 着物 (new) | 初音ミク (+1)
72 | バニーガール | nsfw (-7) | おへそ (-11) | 初音ミク (-25) | OC (-24)
73 | 4コマ | コイカツ! (-7) | 背景 (+3) | 黒髪 (+44) | 下着 (+22)
74 | cute | ファンアート (+4) | 艦隊これくしょん (-6) | cute (+8) | furry (-18)
75 | 極上の乳 | 全裸 (+7) | 魅惑のふともも (+38) | ふたなり (-14) | メイド (+24)
76 | oc | 背景 (+7) | アズールレーン (-14) | 風景 (-20) | 腐向け (-8)
77 | プロセカ | SPY×FAMILY (-28) | 魅惑の谷間 (+10) | 貧乳 (+13) | 3D (+3)
78 | ファンアート | 極上の乳 (-3) | 金髪 (+60) | VOCALOID (-7) | おへそ (+10)
79 | 腋 | オリジナルキャラクター (+6) | 触手 (+40) | ふともも (+5) | 推しの子 (new)
80 | 鬼滅の刃 | oc (-4) | commission (-11) | 3D (+20) | Breasts (new)
81 | ラブライブ! | drawing (+3) | ワンピース (+88) | お尻 (+37) | 裸足 (+3)
82 | 全裸 | NTR (+16) | cute (+15) | 露出 (+31) | ふたなり (-7)
83 | 背景 | sexy (+21) | スレッタ・マーキュリー (new) | 極上の女体 (new) | ショタ (-21)
84 | drawing | 二次創作 (+5) | ふともも (-52) | 裸足 (+2) | 魅惑の顔 (+49)
85 | オリジナルキャラクター | loli (+11) | ファンタジー (+4) | 猫耳 (-27) | 白髪 (+18)
86 | リョナ | リョナ (=) | 裸足 (-29) | アズールレーン (-10) | 艦これ (-23)
87 | 女子高生 | 魅惑の谷間 (+22) | バニーガール (+61) | art (-19) | アークナイツ (-23)
88 | ブルアカ | 4コマ (-15) | 原创 (+7) | おへそ (-16) | お尻 (-7)
89 | 二次創作 | ファンタジー (+8) | 褐色 (+39) | ビキニ (+26) | パンツ (+20)
90 | むちむち | 尻神様 (+9) | 貧乳 (new) | チェンソーマン (-46) | にじさんじ (-20)
Table 2. Monthly ranking of the top 90 tags.