Publications

2025

1. The Rise of Synthetic Grassroots: Characterizing AI-Generated Political Discourse on Reddit

   Yiming Zhu, Gareth Tyson, Yupeng He, and Pan Hui

   2025
2. Beyond Human-Made Beats: Measuring and Detecting AI-Generated Music on SoundCloud

   Yupeng He^*, Yiming Zhu^*, Pan Hui, and Gareth Tyson

   2025
3. Characterizing LLM-driven Social Network: The Chirper.ai Case

   Yiming Zhu, Yupeng He, Ehsan-Ul Haq, Gareth Tyson, and Pan Hui

   2025

   arXiv
4. The Invisible Hand: Characterizing Generative AI Adoption and its Effects on An Online Freelancing Market

   Yiming Zhu, Gareth Tyson, and Pan Hui

   2025

   arXiv
5. Exploring the Capability of ChatGPT to Reproduce Human Labels for Social Computing Tasks

   Yiming Zhu, Peixian Zhang, Ehsan-Ul Haq, Pan Hui, and Gareth Tyson

   In Social Networks Analysis and Mining, 2025

   Abs Website

   Harnessing the potential of large language models (LLMs) like ChatGPT can help address social challenges through inclusive, ethical, and sustainable means. In this paper, we investigate the extent to which ChatGPT can annotate data for social computing tasks, aiming to reduce the complexity and cost of undertaking web research. To evaluate ChatGPT’s potential, we re-annotate seven datasets using ChatGPT, covering topics related to pressing social issues like COVID-19 misinformation, social bot deception, cyberbully, clickbait news, and the Russo-Ukrainian War. Our findings demonstrate that ChatGPT exhibits promise in handling these data annotation tasks, albeit with some challenges. Across the seven datasets, ChatGPT achieves an average annotation F1-score of 72.00%. Its performance excels in clickbait news annotation, correctly labeling 89.66% of the data. However, we also observe significant variations in performance across individual labels. We believe that this research opens new avenues for analysis and can reduce barriers to engaging in social computing research.

2024

1. Exploring the Use of Abusive Generative AI Models on Civitai

   Yiluo Wei^*, Yiming Zhu^*, Pan Hui, and Gareth Tyson

   In Proceedings of the 32nd ACM International Conference on Multimedia, Melbourne VIC, Australia, 2024

   Abs DOI

   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.

2. APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation using ChatGPT

   Yiming Zhu^*, Zhizhuo Yin^*, Gareth Tyson, Ehsan-Ul Haq, Lik-Hang Lee, and Pan Hui

   In Proceedings of the ACM Web Conference 2024, Singapore, Singapore, 2024

   Abs DOI

   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.

3. History in Making: Political Campaigns in the Era of Artificial Intelligence-Generated Content

   Ehsan-Ul Haq, Yiming Zhu, Pan Hui, and Gareth Tyson

   In Companion Proceedings of the ACM Web Conference 2024, Singapore, Singapore, 2024

   Abs DOI

   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.

4. A Study of Partisan News Sharing in the Russian Invasion of Ukraine

   Yiming Zhu, Ehsan-Ul Haq, Gareth Tyson, Lik-Hang Lee, Yuyang Wang, and Pan Hui

   Proceedings of the International AAAI Conference on Web and Social Media, May 2024

   DOI Website
5. Understanding Characteristics of Catalyst Users in the WallStreetBets Community

   Ehsan-Ul Haq, Yiming Zhu, Zijun Lin, Haodi Weng, Gareth Tyson, Lik-Hang Lee, Reza Hadi Mogavi, Tristan Braud, and Pan Hui

   In Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Kusadasi, Turkiye, May 2024

   Abs DOI

   WallStreetBets (WSB), a Reddit community, impacted stock markets during the 2021 GameStop Short Squeeze. We examine the content and user properties that influence engagement in WSB. Despite WSB’s association with emojis and informal terms, engagement among community members depends on more than surface-level factors. Although emojis are commonly used, they are not as effective at fostering interactions among users. Community members engage more with posts that have longer and topic-specific text. Simply producing a high volume of posts is not enough to attract an audience. Consistent topical focus, reciprocal interactions, and previous authorship of catalyst posts influence engagement. WSB posts, regardless of length, generally remain relevant to the community’s theme of stock trading. Our findings provide insights into WSB engagement patterns and can be useful for downstream research, such as financial predictive tasks using WSB data.

6. Echo Chambers within the Russo-Ukrainian War: The Role of Bipartisan Users

   Peixian Zhang, Ehsan Ul Haq, Yiming Zhu, Pan Hui, and Gareth Tyson

   In Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Kusadasi, Turkiye, May 2024

   Abs DOI

   The ongoing Russia-Ukraine war has been extensively discussed on social media. One commonly observed problem in such discourse is the emergence of echo chambers, where users are rarely exposed to opinions outside their own worldview. Prior literature on this topic has assumed that such users hold a single consistent view. However, recent work has revealed that complex topics often trigger bipartisanship among certain people. With this in mind, we study the presence of echo chambers on Twitter related to the Russo-Ukrainian war. We measure their presence and identify an important subset of bipartisan users who vary their opinion during the invasion. We then explore the role they play in the communications graph and their impact on echo chambers.

2021

1. Lessons learned from migrating complex stateful applications onto serverless platforms

   Zewen Jin, Yiming Zhu, Jiaan Zhu, Dongbo Yu, Cheng Li, Ruichuan Chen, Istemi Ekin Akkus, and Yinlong Xu

   In Proceedings of the 12th ACM SIGOPS Asia-Pacific Workshop on Systems, Hong Kong, China, May 2021

   Abs DOI

   Serverless computing is increasingly seen as a pivot cloud computing paradigm that has great potential to simplify application development while removing the burden of operational tasks from developers. Despite these advantages, the use of serverless computing has been limited to few application scenarios exhibiting stateless and parallel executions. In addition, the significant effort and cost associated with rearchitecting existing codebase limits the range of these applications and hinder efforts to enhance serverless computing platforms to better suit the needs of current applications.In this paper, we report our experience and observations from migrating four complex and stateful microservice applications (involving 8 programming languages, 5 application frameworks, and 40 application logic services) to ApacheOpenWhisk, a widely used serverless computing platform. We highlight a number of patterns and guidelines that facilitate this migration with minimal code changes and practical performance considerations, and imply a path towards further automating this process. We hope our guidelines will help increase the applicability of serverless computing and improve serverless platforms to be more application friendly.

2. Press-n-Paste: Copy-and-Paste Operations with Pressure-sensitive Caret Navigation for Miniaturized Surface in Mobile Augmented Reality

   Lik Hang Lee, Yiming Zhu, Yui-Pan Yau, Pan Hui, and Susanna Pirttikangas

   Proc. ACM Hum.-Comput. Interact., May 2021

   Abs DOI

   Copy-and-paste operations are the most popular features on computing devices such as desktop computers, smartphones and tablets. However, the copy-and-paste operations are not sufficiently addressed on the Augmented Reality (AR) smartglasses designated for real-time interaction with texts in physical environments. This paper proposes two system solutions, namely Granularity Scrolling (GS) and Two Ends (TE), for the copy-and-paste operations on AR smartglasses. By leveraging a thumb-size button on a touch-sensitive and pressure-sensitive surface, both the multi-step solutions can capture the target texts through indirect manipulation and subsequently enables the copy-and-paste operations. Based on the system solutions, we implemented an experimental prototype named Press-n-Paste (PnP). After the eight-session evaluation capturing 1,296 copy-and-paste operations, 18 participants with GS and TE achieve the peak performance of 17,574 ms and 13,951 ms per copy-and-paste operation, with 93.21% and 98.15% accuracy rates respectively, which are as good as the commercial solutions using direct manipulation on touchscreen devices. The user footprints also show that PnP has a distinctive feature of miniaturized interaction area within 12.65 mm * 14.48 mm. PnP not only proves the feasibility of copy-and-paste operations with the flexibility of various granularities on AR smartglasses, but also gives significant implications to the design space of pressure widgets as well as the input design on smart wearables.

2020

1. One-thumb Text Acquisition on Force-assisted Miniature Interfaces for Mobile Headsets

   Lik-Hang LEE, Yiming ZHU, Yui-Pan YAU, Tristan BRAUD, Xiang SU, and Pan Hui

   In 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom), May 2020

   DOI