Fake News Detection For Portuguese Language
| dc.contributor.author | Tembe, L.A. | |
| dc.contributor.author | Anand Kumar, M. | |
| dc.date.accessioned | 2026-02-06T06:34:46Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | In this research, we employ Deep Learning to distinguish between true and false news. These approaches are used to identify false information on both trustworthy and shady platforms and sources. These models utilise various Deep Learning approaches to determine a predetermined frequency and news count. We used a wide range of labelled data to train the model. The dataset was chosen from hugging faces and consists of fake news with 20478 entries and True news with 2720 entries. We will use different news outlets, like Twitter and Facebook, to analyse the news to determine if it is true or false. Overall, tree-based LSTM, Bidirectional LSTM model and Bayesian LSTM exhibit superior accuracy. © 2023 IEEE. | |
| dc.identifier.citation | 12th IEEE International Conference on Advanced Computing, ICoAC 2023, 2023, Vol., , p. - | |
| dc.identifier.uri | https://doi.org/10.1109/ICoAC59537.2023.10249998 | |
| dc.identifier.uri | https://idr.nitk.ac.in/handle/123456789/29419 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.subject | Bidirectional LSTM,Bayesian LSTM | |
| dc.subject | Deep learning | |
| dc.subject | Long Short-Term Memory | |
| dc.title | Fake News Detection For Portuguese Language |