James Y. Zhu

The rise of generative AI is transforming the landscape of digital imagery, and exerting a significant influence on online creative communities. This has led to the emergence of AI-Generated Content (AIGC) social platforms, such as Civitai. These distinctive social platforms allow users to build and share their own generative AI models, thereby enhancing the potential for more diverse artistic expression. They also provide artists with the means to showcase their creations (generated from the models), engage in discussions, and obtain feedback, thus nurturing a sense of community. Yet, this openness also raises concerns about the abuse of such platforms, e.g., using models to disseminate deceptive deepfakes or infringe upon copyrights. To explore this, we conduct the first comprehensive empirical study of an AIGC social platform, focusing on its use for generating abusive content. As an exemplar, we construct a comprehensive dataset covering Civitai, the largest available AIGC social platform. Based on this dataset of 87K models and 2M images, we explore the characteristics of content and discuss strategies for moderation to better govern these platforms.

Recent research has highlighted the potential of LLMs, like ChatGPT, for performing label annotation on social computing data. However, it is already well known that performance hinges on the quality of the input prompts. To address this, there has been a flurry of research into prompt tuning — techniques and guidelines that attempt to improve the quality of prompts. Yet these largely rely on manual effort and prior knowledge of the dataset being annotated. To address this limitation, we propose APT-Pipe, an automated prompt-tuning pipeline. APT-Pipe aims to automatically tune prompts to enhance ChatGPT’s text classification performance on any given dataset. We implement APT-Pipe and test it across twelve distinct text classification datasets. We find that prompts tuned by APT-Pipe help ChatGPT achieve higher weighted F1-score on nine out of twelve experimented datasets, with an improvement of 7.01% on average. We further highlight APT-Pipe’s flexibility as a framework by showing how it can be extended to support additional tuning mechanisms.

Web 2.0 provided impactful tools, based on user-generated content, for political campaigns and opinion engineering. However, in recent months, AI advances and the ease of access to AI-generated content (AIGC) have led to a paradigm shift in political participation by politicians and electorates alike. This paper aims to explore a historical analysis of this shift. We provide anecdotal evidence of new trends, potential impact, and challenges. We discuss the usage of AIGC in political campaigns, and how AIGC is used as a substitute for incarcerated politicians. Such a usage presents novel ways for leaders to reach the public and keep them politically active. However, AIGC also has risks when used for disinformation, such as DeepFake media and caller bots, to undermine and malign the opponents. On the other hand, the evidence shows that governments can nudge AIGC content by censoring Internet services. We also report challenges facing AIGC usage, such as model bias and hallucinations, along with a governance perspective on ethics and regulations.