JMIR Publications

ABSTRACT

The infodemic created by the COVID-19 pandemic has created several societal issues, including a rise in distrust between the public and health experts and even a refusal of some to accept vaccination; some sources suggest that 1 in 4 Americans will refuse the vaccine. This social concern can be traced to the level of digitization today -- particularly in the form of social media. As social media was the most significant contributing factor to the spread of misinformation, the team decided to examine infodemiology across various text-based platforms (Twitter, 4chan, Reddit, Parler, Facebook, and YouTube). This was done by utilizing a sentiment analysis to compare general posts with key terms flagged as misinformation (all of which concern COVID-19) to determine their verity. In gathering the datasets, both APIs and also pre-existing data compiled by standard scientific third parties were used. It was found that in some cases, misinforming posts can have up to 92.5% more negative sentiment skew compared to accurate posts. From this, the novel Plebeian Algorithm is proposed, which utilizes sentiment analysis and post popularity as metrics to flag a post as misinformation. This algorithm diverges from that of the status quo, as the Plebeian Algorithm uses a democratic process to detect and remove misinformation. A method was constructed in which content deemed misinformation to be removed from the platform is determined by a randomly selected jury of anonymous users. This not only prevents these types of infodemics, but also guarantees a more democratic way of using social media that is beneficial for repairing social trust and encouraging the public's evidence-informed decision making.