TY  - JOUR
AU  - Fedoruk, Benjamin
AU  - Nelson, Harrison
AU  - Frost, Russell
AU  - Fucile Ladouceur, Kai
PY  - 2021
DA  - 2021/12/21
TI  - The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation
JO  - JMIR Form Res
SP  - e32427
VL  - 5
IS  - 12
KW  - infodemiology
KW  - misinformation
KW  - algorithm
KW  - social media
KW  - plebeian
KW  - natural language processing
KW  - sentiment analysis
KW  - sentiment
KW  - trust
KW  - decision-making
KW  - COVID-19
AB  - Background: 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. Objective: The goal of the research is to determine an optimal social media algorithm, one which is able to reduce the number of cases of misinformation and which also ensures that certain individual freedoms (eg, the freedom of expression) are maintained. After performing the analysis described herein, an algorithm was abstracted. The discovery of a set of abstract aspects of an optimal social media algorithm was the purpose of the study. Methods: 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 using 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 data sets, both application programming interfaces (installed using Python’s pip) and pre-existing data compiled by standard scientific third parties were used. Results: The sentiment can be described using bimodal distributions for each platform, with a positive and negative peak, as well as a skewness. It was found that in some cases, misinforming posts can have up to 92.5% more negative sentiment skew compared to accurate posts. Conclusions: From this, the novel Plebeian Algorithm is proposed, which uses 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 as 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. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2021/12/e32427
UR  - https://doi.org/10.2196/32427
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34854812
DO  - 10.2196/32427
ID  - info:doi/10.2196/32427
ER  -