Related papers: AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images

AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images

URL: http://arxiv.org/abs/2404.14244v3
Date: Thu, 03 Oct 2024 08:22:20 GMT
Title: AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images
Authors: Jonas Ricker, Dennis Assenmacher, Thorsten Holz, Asja Fischer, Erwin Quiring,
Abstract summary: We conduct the first large-scale investigation of the prevalence of AI-generated profile pictures on Twitter. Our analysis of nearly 15 million Twitter profile pictures shows that 0.052% were artificially generated, confirming their notable presence on the platform. The results also reveal several motives, including spamming and political amplification campaigns.
Score: 26.891299948581782
License:
Abstract: Recent advances in the field of generative artificial intelligence (AI) have blurred the lines between authentic and machine-generated content, making it almost impossible for humans to distinguish between such media. One notable consequence is the use of AI-generated images for fake profiles on social media. While several types of disinformation campaigns and similar incidents have been reported in the past, a systematic analysis has been lacking. In this work, we conduct the first large-scale investigation of the prevalence of AI-generated profile pictures on Twitter. We tackle the challenges of a real-world measurement study by carefully integrating various data sources and designing a multi-stage detection pipeline. Our analysis of nearly 15 million Twitter profile pictures shows that 0.052% were artificially generated, confirming their notable presence on the platform. We comprehensively examine the characteristics of these accounts and their tweet content, and uncover patterns of coordinated inauthentic behavior. The results also reveal several motives, including spamming and political amplification campaigns. Our research reaffirms the need for effective detection and mitigation strategies to cope with the potential negative effects of generative AI in the future.

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