Large Language Models (LLMs) offer significant potential for assisting with the design and implementation of social platform moderation. This study evaluates their efficacy as both a replacement to and an augmentation for human moderators. Using Reddit as a case study, we first demonstrate that LLMs can effectively replicate human moderation decisions, achieving 83.9% agreement. Through a mix of LLMs and human annotations, we then evaluate real moderator decisions, uncovering substantial error rates: 15.2% of removals and 13% of approvals are estimated as incorrect, primarily stemming from moderators citing the incorrect rules (84.3% of errors). This motivates us to propose RuleSharpener, a tool that uses LLMs to diagnose the root causes of moderation errors (e.g. ambiguous rules) and generate clearer, more actionable guidelines. Our evaluation shows that RuleSharpener increases the accuracy of identifying the specific rules violated by violation posts by 38.0%. Our work demonstrates how LLMs can augment human moderation, refine community policies, and reduce operational burdens, offering a better solution for platform governance on the web.

Livestreaming by VTubers --- animated 2D/3D avatars controlled by real individuals --- have recently garnered substantial global followings and achieved significant monetary success. Despite prior research highlighting the importance of realism in audience engagement, VTubers deliberately conceal their identities, cultivating dedicated fan communities through virtual personas. While previous studies underscore that building a core fan community is essential to a streamer's success, we lack an understanding of the characteristics of viewers of this new type of streamer. Gaining a deeper insight into these viewers is critical for VTubers to enhance audience engagement, foster a more robust fan base, and attract a larger viewership. To address this gap, we conduct a comprehensive analysis of VTuber viewers on Bilibili, a leading livestreaming platform where nearly all VTubers in China stream. By compiling a first-of-its-kind dataset covering 2.7M livestreaming sessions, we investigate the characteristics, engagement patterns, and influence of VTuber viewers. Our research yields several valuable insights, which we then leverage to develop a tool to ''recommend'' future subscribers to VTubers. By reversing the typical approach of recommending streams to viewers, this tool assists VTubers in pinpointing potential future fans to pay more attention to, and thereby effectively growing their fan community.

Recently, a distinct form of online antisocial behavior, known as ''fanchuan'', has emerged across online platforms, particularly in livestreaming chats. Fanchuan is an indirect attack on a specific entity, such as a celebrity, video game, or brand. It entails two main actions: (i) individuals first feign support for the entity, and exhibit this allegiance widely; (ii) they then engage in offensive or irritating behavior, attempting to undermine the entity by association. This deceptive conduct is designed to tarnish the reputation of the target and/or its fan community. Fanchuan is a novel, covert and indirect form of social attack, occurring outside the targeted community (often in a similar or broader community), with strategic long-term objectives. This distinguishes fanchuan from other types of antisocial behavior and presents significant new challenges in moderation. We argue it is crucial to understand and combat this new malicious behavior. Therefore, we conduct the first empirical study on fanchuan behavior in livestreaming chats, focusing on Bilibili, a leading livestreaming platform in China. Our dataset covers 2.7 million livestreaming sessions on Bilibili, featuring 3.6 billion chat messages. We identify 130k instances of fanchuan behavior across 37.4k livestreaming sessions. Through various types of analysis, our research offers valuable insights into fanchuan behavior and its perpetrators.

The centralization of web services has raised concerns about critical single points of failure, such as content hosting, name resolution, and certification. To address these issues, the "Decentralized Web" movement advocates for de-centralized alternatives. Distributed Hash Tables (DHTs) have emerged as a key component facilitating this movement, as they offer efficient key/value indexing. The InterPlanetary File System (IPFS) exemplifies this approach by leveraging DHTs for data indexing and distribution. A critical finding of previous studies is that DHT PUT performance for record storage is unacceptably slow, sometimes taking minutes to complete and hindering the adoption of delay-intolerant applications. To address this challenge, this research paper presents three significant contributions. First, we present the design of Optimistic Provide, an approach to accelerate DHT PUT operations in Kademlia-based IPFS networks while maintaining full backward compatibility. Second, we implement and deploy the mechanism and see its usage in the de-facto IPFS deployment, Kubo. Third, we evaluate its effectiveness in the IPFS and Filecoin DHTs. We confirm that we enable sub-second record storage from North America and Europe for 90% of PUT operations while reducing networking overhead by over 40% and maintaining record availability.

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.

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.