A Fake News Detection System based on Combination of Word Embedded Techniques and Hybrid Deep Learning Model

At present, most people prefer using different online sources for reading news. These sources can easily spread fake news for several malicious reasons. Detecting this unreliable news is an important task in the Natural Language Processing (NLP) field. Many governments and technology companies are engaged in this research field to prevent the manipulation of public opinion and spare people and society the huge damage that can result from the spreading of misleading information on online social media. In this paper, we present a new deep learning method to detect fake news based on a combination of different word embedding techniques and a hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BILSTM) model. We trained the classification model on the unbiased dataset WELFake. The best method was a combination of a pre-trained Word2Vec CBOW model and a Word2Vec Skip-Word model with a CNN on BILSTM layers, yielding an accuracy of up to 97%.

@article{OUASSIL2022,
  title     = {A Fake News Detection System based on Combination of Word Embedded Techniques and Hybrid Deep Learning Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {10},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Mohamed-Amine OUASSIL and Bouchaib CHERRADI and Soufiane HAMIDA and Mouaad ERRAMI and Oussama EL GANNOUR and Abdelhadi RAIHANI},
  doi       = {10.14569/IJACSA.2022.0131061},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131061}
}