Multimodal Amharic Fake News Detection using CNN-BiLSTM
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Date
2024-06
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Addis Ababa University
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.
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Keywords
Amharic Multimodal Fake News, Unimodal, CNN-BiLSTM