  Following recent outbreaks, monkeypox-related misinformation continues to
rapidly spread online. This negatively impacts response strategies and
disproportionately harms LGBTQ+ communities in the short-term, and ultimately
undermines the overall effectiveness of public health responses. In an attempt
to combat monkeypox-related misinformation, we present PoxVerifi, an
open-source, extensible tool that provides a comprehensive approach to
assessing the accuracy of monkeypox related claims. Leveraging information from
existing fact checking sources and published World Health Organization (WHO)
information, we created an open-sourced corpus of 225 rated monkeypox claims.
Additionally, we trained an open-sourced BERT-based machine learning model for
specifically classifying monkeypox information, which achieved 96%
cross-validation accuracy. PoxVerifi is a Google Chrome browser extension
designed to empower users to navigate through monkeypox-related misinformation.
Specifically, PoxVerifi provides users with a comprehensive toolkit to assess
the veracity of headlines on any webpage across the Internet without having to
visit an external site. Users can view an automated accuracy review from our
trained machine learning model, a user-generated accuracy review based on
community-member votes, and have the ability to see similar, vetted, claims.
Besides PoxVerifi's comprehensive approach to claim-testing, our platform
provides an efficient and accessible method to crowdsource accuracy ratings on
monkeypox related-claims, which can be aggregated to create new labeled
misinformation datasets.

Computation and Language;Machine Learning;Social and Information Networks