Detecting false information on social media using contextual features

Abstract

The spread of fake news on the internet is a major challenge. Traditional fact-checking methods, which rely on human experts to verify information credibility, are not scalable to the volume of online content. Automated fake news detection is a more scalable approach that uses artificial intelligence to learn and identify patterns from news data that are more likely to be fake. However, despite its advantages in terms of accuracy and response time, supervised learning solutions in automated fake news detection have failed to get ahead in identifying fake content across cultures and languages due to the limited availability of fake-checked datasets and biases in the data. To address fake news detection biases, this study proposes a comprehensive approach to fake news detection that combines supervised and unsupervised learning strategies. The first strategy involves supervised automatic fact-checking using a Transformer model and other models including a Logistic classifier, SVM classifier, Convolutional Neural Network classifier, Naive Bayesian classifier, and XGboost classifier. These models are trained on labeled datasets to identify fake news with high accuracy. The second strategy involves unsupervised learning, which is used to handle unlabeled datasets. This strategy allows us to effectively explore and benefit from a wide range of information and insightful facts to our supervised classifiers. We evaluate our approach on two datasets, the LIAR dataset and the ISOT Fake News Dataset. We achieve an accuracy of up to 91% on the ISOT Fake News Dataset through unsupervised relabeling. We also achieve a 100% F1-score in a supervised learning experiment, which means with the real labels of the ISOT dataset. Our study contributes to the development of effective strategies for combating fake news, addressing the challenges posed by the growing digital area nowadays. Keywords: Fake news, Misinformation, Fact-checking, Supervised learning, Unsupervised learning, Transformers.