Multimodal Amharic Fake News Detection using CNN-BiLSTM

dc.contributor.advisorFitsum Assamnew
dc.contributor.authorMekdim Tessema
dc.date.accessioned2024-07-31T08:22:28Z
dc.date.available2024-07-31T08:22:28Z
dc.date.issued2024-06
dc.description.abstractWith 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.urihttps://etd.aau.edu.et/handle/123456789/3312
dc.language.isoen_US
dc.publisherAddis Ababa University
dc.subjectAmharic Multimodal Fake News
dc.subjectUnimodal
dc.subjectCNN-BiLSTM
dc.titleMultimodal Amharic Fake News Detection using CNN-BiLSTM
dc.typeThesis

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Mekdim Tessema.pdf
Size:
1.22 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description: