Fake News Detection Using Convolutional Neural Networks

Authors

  • Saroj Giri Department of Information Technology, Gandaki University, Nepal
  • Shiva Ram Dam Department of Information Technology, Gandaki University, Nepal
  • Rajesh Kamar Department of Information Technology, Gandaki University, Nepal
  • Suraj Basant Tulachan Department of Electronics and Computer Engineering, IoE, Pashchimanchal Campus, Pokhara

DOI:

https://doi.org/10.3126/tj.v4i1.73953

Keywords:

Convolutional Neural Networks, Global Vector, Maxpooling

Abstract

There exist catchy headlines in the digital media so as to lure readers to click on them. These exaggerate the facts and baits the users since the actual contents deviate farther from the clickbait headlines. A Machine learning model has been implemented using Convolutional Neural Network (CNN) to train and test on English dataset and then to perform classification. The CNN model was trained using Keras classifier with Tensor flow as backend. The model was trained and validated with the training dataset and validation dataset. Finally, the model is evaluated with the testing dataset. The CNN model obtained an F1 score of 92 % on test data. Cross validation technique was used for data validation.

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Published

2024-12-31

How to Cite

Giri, S., Dam, S. R., Kamar, R., & Tulachan, S. B. (2024). Fake News Detection Using Convolutional Neural Networks. Technical Journal, 4(1), 64–68. https://doi.org/10.3126/tj.v4i1.73953

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Articles