The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation

Area of Science:

• Infodemiology
• Computational Social Science
• Public Health Communication

Background:

• The COVID-19 pandemic fueled an infodemic, increasing public distrust in health experts and vaccine hesitancy.
• Digitization and social media platforms have exacerbated the spread of health misinformation.
• A significant portion of the population may refuse vaccination due to misinformation.

Purpose of the Study:

• To identify an optimal social media algorithm for reducing misinformation.
• To ensure individual freedoms, such as freedom of expression, are maintained.
• To abstract key aspects of an effective social media algorithm.

Main Methods:

• Analyzed infodemiology across text-based platforms: Twitter, 4chan, Reddit, Parler, Facebook, and YouTube.
• Employed sentiment analysis to compare general posts with COVID-19 misinformation keywords.
• Utilized application programming interfaces (APIs) and pre-existing datasets for data acquisition.

Main Results:

• Sentiment analysis revealed bimodal distributions with positive and negative peaks and skewness across platforms.
• Misinforming posts exhibited up to 92.5% greater negative sentiment skew than accurate posts.
• Identified sentiment and post popularity as key metrics for misinformation detection.

Conclusions:

• Proposed the novel Plebeian Algorithm, utilizing sentiment analysis and post popularity to flag misinformation.
• The Plebeian Algorithm employs a democratic approach, using randomly selected user juries to determine content removal.
• This democratic method aims to prevent infodemics, foster social trust, and encourage evidence-informed decision-making.