Online discourse surrounding geopolitical crises is volatile and complex. For example, users can often change their opinions, and apply rationales divergently based on the specific scenario under discussion. This paper explores such stance and rationale divergence in social media discussions. We focus on two major ongoing conflicts: the Russia-Ukraine and Israel-Palestine wars. Through this, we identify a set of users who discuss both conflicts, and then label each user’s comments with their stance and associated rationale. Using this unique dataset, we explore how people apply rationales divergently, and evolve their opinions over time. Our research contributes to the CHI community by providing a reusable, rationale-level annotation methodology. Our findings can inform the design of moderation tools, recommender systems, and discussion interfaces. These can be used to surface disagreements, calibrate echo-chamber exposure, and ultimately foster healthier online discourse.

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.

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.

This paper presents the first large-scale empirical study of commercial personally identifiable information (PII) removal systems --- commercial services that claim to improve privacy by automating the removal of PII from data broker's databases. Popular examples of such services include DeleteMe, Mozilla Monitor, Incogni, among many others. The claims these services make may be very appealing to privacy-conscious Web users, but how effective these services actually are at improving privacy has not been investigated. This work aims to improve our understanding of commercial PII removal services in multiple ways. First, we conduct a user study where participants purchase subscriptions from four popular PII removal services, and report (i) what PII the service find, (ii) from which data brokers, (iii) whether the service is able to have the information removed, and (iv) whether the identified information actually is PII describing the participant. And second, by comparing the claims and promises the services makes (e.g. which and how many data brokers each service claims to cover). We find that these services have significant accuracy and coverage issues that limit the usefulness of these services as a privacy-enhancing technology. For example, we find that the measured services are unable to remove the majority of the identified PII records from data broker's (48.2% of the successfully removed found records) and that most records identified by these services are not PII about the user (study participants found that only 41.1% of records identified by these services were PII about themselves).

The advent of regulation, such as the Digital Markets Act, will foster greater interoperability across competing digital platforms. In such regulatory environments, decentralized platforms like Mastodon have pioneered the principles of social data portability. Such platforms are composed of thousands of independent servers, each of which hosts their own social community. To enable transparent interoperability, users can easily migrate their accounts from one server provider to another. In this paper, we examine 8,745 users who switch their server instances in Mastodon. We use this as a case study to examine account portability behavior more broadly. We explore the factors that affect users' decision to switch instances, as well as the impact of switching on their social media engagement and discussion topics. This leads us to build a classifier to show that switching is predictable, with an F1 score of 0.891. We argue that Mastodon serves as an early exemplar of a social media platform that advocates account interoperability and portability. We hope that this study can bring unique insights to a wider and open digital world in the future.

The acquisition of Twitter by Elon Musk has spurred controversy and uncertainty among Twitter users. The move raised both praise and concerns, particularly regarding Musk's views on free speech. As a result, a large number of Twitter users have looked for alternatives to Twitter. Mastodon, a decentralized micro-blogging social network, has attracted the attention of many users and the general media. In this paper, we analyze the migration of 136,009 users from Twitter to Mastodon. We inspect the impact that this has on the wider Mastodon ecosystem, particularly in terms of user-driven pressure towards centralization. We further explore factors that influence users to migrate, highlighting the effect of users' social networks. Finally, we inspect the behavior of individual users, showing how they utilize both Twitter and Mastodon in parallel. We find a clear difference in the topics discussed on the two platforms. This leads us to build classifiers to explore if migration is predictable. Through feature analysis, we find that the content of tweets as well as the number of URLs, the number of likes, and the length of tweets are effective metrics for the prediction of user migration.