research-article

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

Authors:

Gareth TysonAuthors Info & Claims

MM '24: Proceedings of the 32nd ACM International Conference on Multimedia

Pages 6813 - 6822

https://doi.org/10.1145/3664647.3680631

Published: 28 October 2024 Publication History

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.

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Cited By

Wei YZhu YHui PTyson GCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Exploring the Use of Abusive Generative AI Models on CivitaiProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681052(6949-6958)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681052
Li YLiu ZZhao JRen LLi FLuo JLuo B(2024)The Adversarial AI-Art: Understanding, Generation, Detection, and BenchmarkingComputer Security – ESORICS 202410.1007/978-3-031-70879-4_16(311-331)Online publication date: 5-Sep-2024
https://doi.org/10.1007/978-3-031-70879-4_16

Index Terms

Understanding the Impact of AI-Generated Content on Social Media: The Pixiv Case
1. Human-centered computing
  1. Collaborative and social computing
    1. Empirical studies in collaborative and social computing

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cover image ACM Conferences

MM '24: Proceedings of the 32nd ACM International Conference on Multimedia

October 2024

11719 pages

ISBN:9798400706868

DOI:10.1145/3664647

General Chairs:
Jianfei Cai
Monash University, Australia
,
Mohan Kankanhalli
NUS, Singapore
,
Balakrishnan Prabhakaran
UT Dallas, USA
,
Susanne Boll
University of Oldenburg, Germany
,
Program Chairs:
Ramanathan Subramanian
University of Canberra & IIT Ropar, Australia
,
Liang Zheng
Australian National University, Australia
,
Vivek K. Singh
Rutgers University, USA
,
Pablo Cesar
Centrum Wiskunde & Informatica, Netherlands
,
Lexing Xie
Australian National University, Australia
,
Dong Xu
University of Hong Kong, Hong Kong

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Published: 28 October 2024

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MM '24: The 32nd ACM International Conference on Multimedia

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Cited By

Wei YZhu YHui PTyson GCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Exploring the Use of Abusive Generative AI Models on CivitaiProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681052(6949-6958)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681052
Li YLiu ZZhao JRen LLi FLuo JLuo B(2024)The Adversarial AI-Art: Understanding, Generation, Detection, and BenchmarkingComputer Security – ESORICS 202410.1007/978-3-031-70879-4_16(311-331)Online publication date: 5-Sep-2024
https://doi.org/10.1007/978-3-031-70879-4_16

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