| The monkeypox outbreak in 2022 raised uncertainty leading to misinformation and conspiracy narratives in social media. The belief in misinformation leads to poor judgment, decision making, and even to unnecessary loss of life. The ability of misinformation to spread through social media may worsen the harms of different emergencies, and fighting it is therefore critical. In this work, we analyzed the discussion of misinformation related to monkeypox on Twitter by training different classifiers that differentiate between tweets that spread and tweets that counter misinformation. We collected over 1.4M tweets related to the discussion of monkeypox on Twitter from over 500K users and calculated word and sentence embeddings using Natural Language Processing (NLP) methods. We trained multiple machine learning classification models and fine-tuned a Robustly Optimized BERT Pretraining Approach (RoBERTa) model on a set of 3K hand-labeled tweets. We found that the fine-tuned RoBERTa model provided superior results and used it to classify the complete dataset into three categories, misinformation, counter misinformation and neutral. We analyzed the behavioral patterns and domains that were used in misinformation and counter misinformation tweets. The findings provide insights into the scale of misinformation within the discussion on monkeypox and the behavior of tweets and users that spread and counter misinformation over time. In addition, the findings allow us to derive policy recommendations to address misinformation in social media. |
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