Rumor Detection On Social Media

  • Sahithi.K , Keerthi.S , Tejaswi.T, Madhavi Katamaneni
Keywords: Rumor detection,Social Media, Long Short Term Memory Model, Pooling layer in Recurrent Neural Networks, Bidirectional long short-term memory

Abstract

Various microblogging platforms are now being used for all forms of communications. Because of the diverse applications and wide variety of its uses. Social media has become a popular means of contact, and social networking sites have become potential sites for speculation. Fake news can trigger panic and a lack of confidence between the government and the general public.The existing models to detect the rumors in social media using machine learning feature extraction are time consuming, costly and require manual intervention. To detect rumour on social media, our model employs deep learning techniques. We used different deep learning techniques, pooling layer of RNN along with a long short-term memory model and also Bidirectional long short-term memory to reliably discredit the suspicious tweet as rumour or non-rumour.

Published
2021-09-01
How to Cite
Sahithi.K , Keerthi.S , Tejaswi.T, Madhavi Katamaneni. (2021). Rumor Detection On Social Media. Design Engineering, 10672-10679. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/3942
Section
Articles