Improving Cross-Lingual Fake News Detection in Indonesia with a Hybrid Model by Enhancing the Embedding Process

In the digital age, the spread of false information across languages in digital form threatens the authenticity and credibility of information. This study aims to develop an efficient hybrid deep learning model for detecting cross-lingual fake news, particularly in resource-constrained environments, by enhancing the embedding process. It proposes a lightweight model that combines MUSE embeddings with CNN, LSTM, and LSTM-CNN architectures to evaluate performance across various language pairs with Indonesian as the source language. Experiments show that linguistic similarity significantly influences classification performance. CNN achieves an F1-score of 82% for the Indonesian–Malay pair, a similar language pair. While LSTM achieves 97% for the Indonesian–German language pair (a structurally different language pair). These findings highlight the effectiveness of hybrid architectures and multilingual embeddings in improving cross-lingual fake news detection, especially when English is not the source language. The proposed method provides a reliable yet computationally efficient solution for multilingual misinformation detection in resource-constrained environments.

@article{Hakim2025,
  title     = {Improving Cross-Lingual Fake News Detection in Indonesia with a Hybrid Model by Enhancing the Embedding Process},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Jihan Nabilah Hakim and Yuliant Sibaroni},
  doi       = {10.14569/IJACSA.2025.0160668},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160668}
}