Impact of AI Generated Content on Social Media: Insights from Pixiv

Core Concepts

The study analyzes the impact of AI-generated content on social media platforms, focusing on Pixiv. It provides valuable insights into the changes in content creation, consumption patterns, and community dynamics due to the introduction of AIGC.

Abstract

The study delves into the implications of AI-generated content on Pixiv, highlighting changes in content creation volumes, engagement levels of creators, themes diversity, and consumer behavior. It sheds light on the differences between human and AI creators and their interaction patterns.
The research reveals that AIGC has led to a 50% increase in new artworks but no corresponding rise in views or comments. There is a decline in newly registered human creators by 4.3%, indicating an impact on creator engagement. Themes have shifted with less diversity and more adult content and female characters.
Consumption behavior shows that AI-generated artworks are popular in lower percentiles while human-generated ones dominate higher percentiles. Human-created content focuses on popular creators and themes, while AI-generated content is more evenly distributed. Interaction patterns differ between human and AI creators under artworks.

Stats

Pixiv hosts over 100 million artistic submissions.
More than 1 billion page views per month as of 2023.
Dataset includes 15.2 million posts with 2.4 million AI-generated images.
A 50% increase in new artworks after the introduction of AIGC.
A 4.3% decrease in newly registered human creators.
Increase in adult content by 50% and female characters by 30%.

Quotes

"AI creators generate artworks faster but do not upload significantly more artworks than human creators."
"Human-generated content consumption centers around the most popular creators and themes."
"Both human and AI creators are more likely to interact within their respective groups."

Key Insights Distilled From

Understanding the Impact of AI Generated Content on Social Media

by Yiluo Wei,Ga... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18463.pdf

Deeper Inquiries

How can online social platforms effectively manage the rise of AIGC without compromising quality?

To effectively manage the rise of AIGC on online social platforms, several measures can be implemented. Firstly, platforms should establish clear guidelines and policies regarding the use of AI-generated content to ensure that it aligns with community standards and values. This includes implementing mechanisms for users to easily distinguish between AI-generated and human-generated content. Additionally, platforms can leverage AI themselves to detect and flag potentially harmful or inappropriate AIGC.
Furthermore, fostering a supportive environment for human creators is essential in maintaining quality amidst the influx of AIGC. Platforms can provide resources, tools, and training programs to help new creators improve their skills and visibility. Encouraging collaboration between AI creators and human creators can also lead to more diverse and high-quality content on the platform.
Regular monitoring and moderation of content are crucial in ensuring that AIGC does not overshadow or drown out human-created content. Platforms should actively engage with their communities to gather feedback on the impact of AIGC and make necessary adjustments to maintain a balance between AI-generated and human-generated content.

How might the shift towards more uniform popularity distribution impact user engagement over time?

The shift towards a more uniform popularity distribution due to the presence of AIGC could have significant implications for user engagement over time. In a scenario where both AI-generated artworks are evenly distributed in terms of popularity compared to human-created ones, users may experience a broader range of content rather than being concentrated around popular themes or creators.
This could lead to increased exploration by users as they discover new artists or themes that they may not have encountered otherwise. However, it may also result in challenges for platform algorithms in recommending relevant content based on user preferences if there is no clear distinction in popularity levels among different types of artwork.
Moreover, this shift could influence how users interact with each other within the community. With a more even distribution of popular artworks across various categories or themes, discussions among users may become more diverse as individuals explore different types of art styles or subjects.
Overall, while a uniform popularity distribution may promote diversity within an online social platform's ecosystem, it could also pose challenges in catering to individual preferences effectively unless tailored strategies are employed by platforms for personalized recommendations based on user behavior patterns.