Rumor Detection with Twitter and News Channel Data Using Sentiment Analysis and Classification

Abstract

Nowadays peoples are going towards social media increasingly to fetch the information and to share their
opinion on social media. As there is rapid diffusion of information on social media, the information posted on social
media spread so fast and easy. This information posted on social media not always right or not truthful to make sense. An
advantage of social media is that all the people can share information and also gives their opinions on that platform. The
drawback of such rapid diffusion of information is that false information are also spread. As the rumors are spreading on
Twitter and other social media so fast and easier. We need to provide some solutions to detect such rumors. In this paper,
our detection approach is based on the classification. Our detection approach is divided into three parts: Preprocessing,
Sentiment Analysis and Classification. Also we are comparing different supervised learning techniques/methods for
getting better and accurate detection of rumors. We are using one more external module i.e. news websites verification
and comparing sentiment score of our proposed method and sentiment score of this external module.