Multimodal based Amharic fake news detection using CNN and attention-based BiLSTM

Fake news consists of fabricated stories with no verifiable facts, sources, or quotes, often created to mislead readers or for economic gain, such as click bait. While the spread of fake news on social media has been widely recognized for its profound political, economic, and social consequences, existing research on detection methods remains limited in the context of low-resource languages. In particular, studies addressing Amharic are scarce, and to date, no research has investigated multimodal approaches for fake news detection in this language. This study aims to develop a multimodal fake news detection system for Amharic. Data was collected using Face pager and the Facebook Graph API 13.0, resulting in a dataset of 23,856 news stories. Various preprocessing techniques were applied, including text cleaning, tokenization, normalization, and visual content processing involving noise removal, and other image preprocessing techniques. The study evaluates several deep learning architectures, including combinations of CNN with BiLSTM, CNN for both image and text, CNN with CNN-BiLSTM, and CNN with attention-based BiLSTM. Among these, the CNN with attention-based BiLSTM model demonstrated superior performance across all evaluation metrics, achieving an accuracy of 98%. Therefore, we proposed the CNN with attention-based BiLSTM model for multimodal Amharic fake news detection.