Fake News Detection based on Deep Learning
DOI:
https://doi.org/10.54097/fcis.v4i1.9479Keywords:
False News Detection, Tensorflow, CNN and LSTM Fusion, Lightgbm, XGboostAbstract
The rapid popularization of the Internet has broken the professional threshold of information dissemination, enabling more and more people to easily obtain information, share and express views through social media, which has greatly enriched people's daily life. However, due to the huge number of users of social media, false news fabricated for various purposes is emerging in endlessly. Moreover, with the progress of technology, false news is no longer simply spread in the form of text, but more spread through the combination of text, pictures and video, which greatly increases the confusion of false news. The experiment in this paper is based on tensorflow to detect false news. During the experiment, LR was used to obtain the fusion coefficients of CNN and LSTM models, that is the regression coefficient of LR, and then calculated the optimal threshold with the fused model on the verification set. In addition, in terms of model selection, lightgbm and xgboost were selected to train the model on the training set for false news, and predicted the news text on the testing set. The results of three experiments show that the effect of using xgboost model is the best, and the F1 score obtained in the experiment is the highest.
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