Using Machine Learning to Enhance the Quantification of Social Noise on Social Media

Social media has become a fertile ground for spreading misinformation in the form of propaganda, fake news, and conspiracy theories. Given the scale and the importance of the problem, there have been calls for content moderation to combat misinformation. However, given the amount of information shared on social media and the controversies surrounding what constitutes freedom of speech, there is a need for a more practical and effective way to combat misinformation. One approach is the use of social entropy to quantify society to minimize the impact of misinformation and enable the construction of clear and more meaningful messages. This study reports on the results from previous work and proposes a new method using machine learning to enhance the detection of social noise. Social noise plays an important role in magnifying and spreading misinformation by increasing the level of uncertainty and disorder.