Towards automatic fake news classification

Abstract

The interaction of technology with humans has many adverse effects. The rapid growth and outreach of the social media and the Web have led to the dissemination of questionable and untrusted content among a wider audience, which has negatively influenced their lives and judgment. Many research studies have been conducted to tackle the detection and spreading of fake news, which is misinformation that looks genuine. While the first step of such tasks would be to classify claims associated based on their credibility, the next steps would involve identifying hidden patterns in style, syntax, and content of such news claims. We propose a generalized method based on Deep Neural Networks to detect if a given claim is fake or genuine. We have used a modular approach by combining techniques from information retrieval, natural language processing, and deep learning. Our classifier comprises two main submodules. The first submodule uses the claim to retrieve relevant articles from the knowledge base which can then be used to verify the truth of the claim. It also uses word‐level features for prediction. The second submodule uses a deep neural network to learn the underlying style of fake content. Our experiments conducted on benchmark datasets show that for the given classification task we can obtain up to 82.4% accuracy by using a combination of two models; the first model was up to 72% accurate while the second model was around 81% accurate. Our detection model has the potential to automatically detect and prevent the spread of fake news, thus, limiting the caustic influence of technology in the human lives.

FakeBERT: Fake news detection in social media with a BERT-based deep learning approach

“…In this research article, authors have also presented a comprehensive review of detecting fake news on social media, false news classifications on psychology and social concepts, and existing algorithms from a data mining perspective. Ghosh et al [ 14 ] have investigated the impact of web-based social networking on political decisions. Quantity research [ 2 , 53 , 54 ] has been done in the context of detecting political-news-based articles.…”

“…News content-based approaches [ 1 , 14 , 51 , 53 ] deals with different writing style of published news articles. In these techniques, our main focus is to extract several features in fake news article related to both information as well as the writing style.…”

“…In these learnings, style-based methodologies [ 12 , 35 , 53 ] are helpful to capture the writing style of manipulators using linguistic features for identifying fake articles. Thus, it is difficult to detect fake news more accurately by using only news content-based features [ 14 , 33 , 46 ]. Thus, we also need to investigate the engagement of fake news articles with users.…”

“…2 . In most of the existing and useful methods [ 14 , 38 , 51 ] consists of news content and context level features using unidirectional pre-trained word embedding models (such as GloVe, TF-IDF, word2Vec, etc.) There is a large scope to use bidirectional pre-trained word embedding models having powerful feature extraction capability.…”

EchoFakeD: improving fake news detection in social media with an efficient deep neural network

“…News content-based methodologies [ 1 , 9 , 10 ] primarily focus on extracting different features from fake news articles, including both content-based as well as style-based. Information-based methodologies [ 9 , 11 ] plan to utilize external sources to certainty check the honesty of the claims in news content.…”