Analyzing Network Characteristics for Misinformation Detection in Online Social Media

The widespread use of online social media systems has created a breeding ground for the spread of misinformation. This has been a growing problem in recent years as false information could have severe consequences, including influencing public opinion, spreading panic and fear, or even impacting political processes. Detecting and combating misinformation in online social media has become an important challenge for researchers and social media platforms. In this technical abstract, we propose a method for detecting and preventing misinformation in online social media by analyzing network characteristics. Our approach uses network analysis techniques to identify patterns and anomalies in the structure and behavior of online social networks that are indicative of the presence of misinformation. This includes analyzing user connectivity, interactions, and engagement dynamics in online social networks to detect suspicious activities and identify potential sources of misinformation. Our method is based on the hypothesis that the spread of misinformation is fundamentally different from the spread of reliable information. Misinformation tends to spread quickly and widely through networks due to its sensational and attention-grabbing nature. This results in distinct network characteristics, including high levels of connectivity within specific groups, an unusually high number of followers for a single person, and a lack of diversity in sources of information.