PoxVerifi: An Information Verification System to Combat Monkeypox Misinformation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 4 of its 49 references that are in the map. First posted on arXiv 9 Sep 2022.
Builds on
4 of 49 references.
References · 4
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2017Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a surveycited by 90
- 2020BRENDA: Browser Extension for Fake News Detectioncited by 20
- 2022A CNN-LSTM-based hybrid deep learning approach to detect sentiment polarities on Monkeypox tweetscited by 3
Built on by
Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.