Multimodal Amharic Fake News Detection using CNN-BiLSTM
| dc.contributor.advisor | Fitsum Assamnew | |
| dc.contributor.author | Mekdim Tessema | |
| dc.date.accessioned | 2024-07-31T08:22:28Z | |
| dc.date.available | 2024-07-31T08:22:28Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | With the growth of internet accessibility social media users increased rapidly in Ethiopia. This created an easy ground for transmission of information between people. On the flip side it became a hub for fake news fabrication and propagation. Fake news that is available online has the potential to cause significant issues for both individuals and society as a whole. We propose a multimodal fake news detection for Amharic on social media that combines textual and visual features. Genuine and fake news data was collected from social media to create multimodal Amharic news dataset. The collected data was preprocessed to retrieve textual and visual features using Bidirectional Long Short Term Memory (BiLSTM) and Conventional Neural Network (CNN) respectively. Then the two sets of features were concatenated and were used to train our multimodal fake news detection model. Our proposed method achieved a 90% accuracy, 94% Precision. Compared to the state of the art unimodal fake news detection for Amharic, our proposed model achieved 4% accuracy and 7% precision increase in fake news detection performance. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/3312 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Amharic Multimodal Fake News | |
| dc.subject | Unimodal | |
| dc.subject | CNN-BiLSTM | |
| dc.title | Multimodal Amharic Fake News Detection using CNN-BiLSTM | |
| dc.type | Thesis |