Combating Misinformation on Social Media Using Social Noise and Social Entropy as a Measure of Uncertainty

Abstract

Recent events around the world, including the war in Ukraine and the conflict in Gaza have highlighted the effective use of social media as a tool to voice concerns about social issues to create awareness. At the same time, social media has become a fertile ground for spreading misinformation, fake news, and conspiracy theories. Misinformation and conspiracy theories have existed since the existence of mankind. What is new today is the speed by which misinformation can be created, magnified and spread using social media. Efforts to regulate social media and control the widespread spread of misinformation are still lacking due to rapid advances in technology and concerns regarding free speech. One approach to minimizing the impact of misinformation is to focus on social noise as an important factor in magnifying and spreading misinformation. In this paper, we investigate methods of identifying and quantifying social noise using social entropy as a measure of uncertainty and topic modeling. Results from the study have shown a direct relationship between social noise and social entropy. The results have also shown that social noise and social entropy decrease with the use of URLs and rich content (sematic information). Further studies will include the use of machine learning and AI techniques to improve the definition of social news and social entropy.