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Deep Learning for Fake News Classification

Molina-Solana, Miguel and Amador Diaz Lopez, Julio and Gómez-Romero, Juan
Proc. I workshop in Deep Learning (DEPL 2018) , , pp. (2018)

Abstract:

This work presents the application of several Deep Learning techniques for Natural Language Processing to the classification of tweets into containing fake news or not. To validate our approach, we use an open-access dataset containing annotated tweets related to the 2016 US elections. From our experiments, we can confirm that Deep Learning techniques are indeed able to identify tweets containing fake news, and that LSTMs with pre-computed embeddings is the best performing among the tested techniques (validation AUC = 0.70), particularly in avoiding misclassification of the minority class.

Bibtex:

@inproceedings{caepia2018_fakenews,
  title = {Deep Learning for Fake News Classification},
  author = {Molina-Solana, Miguel and Amador~Diaz~Lopez, Julio and G\'omez-Romero, Juan},
  booktitle = {Proc. I workshop in Deep Learning (DEPL 2018)},
  year = {2018},
  address = {Granada, Spain},
  month = oct,
  timestamp = {49},
  url = {http://caepia18.aepia.org}
}