Context-Based Fake News Detection Model Relying on Deep Learning Models

Abstract

Nowadays, due to the great accessibility to the internet, people seek out and consume news via social media due to its low cost, ease of access, and quick transmission of information. The tremendous leverage of social media applications in daily life makes them significant information sources. Users can post and share different types of information in all their forms with a single click. However, the cost becomes expensive and dangerous when non-experts say anything about anything. Fake news are rapidly dominating the dissemination of disinformation by distorting people’s views or knowledge to influence their awareness and decision-making. Therefore, we have to identify and prevent the problematic effects of falsified information as soon as possible. In this paper, we conducted three experiments with machine learning classifiers, deep learning models, and transformers. In all experiments, we relied on word embedding to extract contextual features from articles. Our experimental results showed that deep learning models outperformed machine learning classifiers and the BERT transformer in terms of accuracy. Moreover, results showed almost the same accuracy between the LSTM and GRU models. We showed that by combining an augmented linguistic feature set with machine or deep learning models, we can, with high accuracy, identify fake news.