Scopus Indexed Publications
Paper Details
- Title
- Multi Class Fake News Detection using LSTM Approach
- Author
- Bhaskar Majumdar, Md. Arid Hasan, Md. Rafiuzzaman Bhuiyan, Md. Sanzidul Islam, Sheak Rashed Haider Noori,
- bhaskar15-9988@diu.edu.bd
- Abstract
- Nowadays the spread of fake news or information is having a detrimental effect on society. Due to the widespread spread of fake news, we sometimes believe a lot of fake news is true. As a result, we face issues and deprive ourselves of a lot of good and realistic news. To protect people’s lives from these various problems, we need to work to automatically detect fake news. Fake news detection is very complex task. In this paper we present our approach to address multi class fake news detection using Deep Learning. We used a Long Short Term Memory (LSTM) model for multi class fake news detection using data provided by the task organizers. Our best performing model on the training data achieved an accuracy of 0.98. Our trained model gave an accurate response to the detection of fake news.
- Keywords
- LSTM , Deep learning , Spread , Fake News , Detection
- Journal or Conference Name
- 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)
- Publication Year
- 2021
- Indexing
- scopus