A Proposed Model for Preventing the spread of misinformation on Online Social Media using Machine Learning

Tyagi, S.; Pai, A.; Pegado, J.; Kamath, A.

With more than 71% of internet users using Online Social Media (OSM), it has become an important platform for people to share ideas, information and various forms of expressions. However, there is no guarantee about the credibility of the information i.e. how legitimate is the information due to the use of crowd sourcing and absence of any central moderation. This makes it easier for malicious users and some anti-social elements to circulate rumors and create panic among the public, particularly during any real time incident or a disaster by generating fake content. Among the OSMs, the most popular micro-blogging website, Twitter, becomes an easy target for malicious users to spread misinformation having a wide variety of crowd from general public to celebrities, politicians and even large organizations. The system aims to detect such misleading information on Twitter and provide possible measures that can be adopted by the social media company to prevent the spread of misinformation and by the users who contribute to the spread without verifying the veracity of the content.