Combining Vagueness Detection with Deep Learning to Identify Fake News
This incremental approach addresses the problem of identifying fake news for media and fact-checking applications by integrating complementary techniques.
The paper tackled fake news detection by combining a rule-based vagueness detection algorithm (VAGO) with a deep learning classifier (FAKE-CLF), finding a positive correlation between vagueness measures and biased text classification across four corpora.
In this paper, we combine two independent detection methods for identifying fake news: the algorithm VAGO uses semantic rules combined with NLP techniques to measure vagueness and subjectivity in texts, while the classifier FAKE-CLF relies on Convolutional Neural Network classification and supervised deep learning to classify texts as biased or legitimate. We compare the results of the two methods on four corpora. We find a positive correlation between the vagueness and subjectivity measures obtained by VAGO, and the classification of text as biased by FAKE-CLF. The comparison yields mutual benefits: VAGO helps explain the results of FAKE-CLF. Conversely FAKE-CLF helps us corroborate and expand VAGO's database. The use of two complementary techniques (rule-based vs data-driven) proves a fruitful approach for the challenging problem of identifying fake news.