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QAnon is a far-right conspiracy theory whose followers largely organize online. In this work, we use web crawls seeded from two of the largest QAnon hotbeds on the Internet, Voat and 8kun, to build a QAnon-centered domain-based hyperlink graph. We use this graph to identify, understand, and learn about the set of websites that spread QAnon content online. Specifically, we curate the largest list of QAnon centered websites to date, from which we document the types of QAnon sites, their hosting providers, as well as their popularity. We further analyze QAnon websites' connection to mainstream news and misinformation online, highlighting the outsized role misinformation websites play in spreading the conspiracy. Finally, we leverage the observed relationship between QAnon and misinformation sites to build a highly accurate random forest classifier that distinguishes between misinformation and authentic news sites. Our results demonstrate new and effective ways to study the growing presence of conspiracy theories and misinformation on the Internet.

Certificate Authorities (CAs) regularly make mechanical errors when issuing certificates. To quantify these errors, we introduce ZLint, a certificate linter that codifies the policies set forth by the CA/Browser Forum Baseline Requirements and RFC 5280 that can be tested in isolation. We run ZLint on browser-trusted certificates in Censys and systematically analyze how well CAs construct certificates. We find that the number errors has drastically reduced since 2012. In 2017, only 0.02% of certificates have errors. However, this is largely due to a handful of large authorities that consistently issue correct certificates. There remains a long tail of small authorities that regularly issue non-conformant certificates. We further find that issuing certificates with errors is correlated with other types of mismanagement and for large authorities, browser action. Drawing on our analysis, we conclude with a discussion on how the community can best use lint data to identify authorities with worrisome organizational practices and ensure long-term health of the Web PKI.Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.

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In this paper, we explore the feasibility of leveraging large language models (LLMs) to automate or otherwise assist human raters with identifying harmful content including hate speech, harassment, violent extremism, and election misinformation. Using a dataset of 50,000 comments, we demonstrate that LLMs can achieve 90% accuracy when compared to human verdicts. We explore how to best leverage these capabilities, proposing five design patterns that integrate LLMs with human rating, such as pre-filtering non-violative content, detecting potential errors in human rating, or surfacing critical context to support human rating. We outline how to support all of these design patterns using a single, optimized prompt. Beyond these synthetic experiments, we share how piloting our proposed techniques in a real-world review queue yielded a 41.5% improvement in optimizing available human rater capacity, and a 9-11% increase (absolute) in precision and recall for detecting violative content.

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