Linguistic Features and Bi-LSTM for Identification of Fake News

Abstract

With the spread of Internet technologies, the use of social media has increased exponentially. Although social media has many benefits, it has become the primary source of disinformation or fake news. The spread of fake news is creating many societal and economic issues. It has become very critical to develop an effective method to detect fake news so that it can be stopped, removed or flagged before spreading. To address the challenge of accurately detecting fake news, this paper proposes a solution called Statistical Word Embedding over Linguistic Features via Deep Learning (SWELDL Fake), which utilizes deep learning techniques to improve accuracy. The proposed model implements a statistical method called “principal component analysis” (PCA) on fake news textual representations to identify significant features that can help identify fake news. In addition, word embedding is employed to comprehend linguistic features and Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify news as true or fake. We used a benchmark dataset called SWELDL Fake to validate our proposed model, which has about 72,000 news articles collected from different benchmark datasets. Our model achieved a classification accuracy of 98.52% on fake news, surpassing the performance of state-of-the-art deep learning and machine learning models.

An efficient fake news classification model based on ensemble deep learning techniques

“…(1,2) Fake news implies spread of false information on social media with the intention of confusing or misinforming readers in order to further commercial or political objectives. (3) Additionally, the sector of news authoring and distribution is seeing an increase in a variety of players, which has produced news articles that are hard to determine whether they are legitimate or not. Researchers from academia and business are searching for solutions to halt the massive dissemination of false information on social media.…”

“…p (positive∖w) =(P(Positive ⋂w))/(p(w))= ⋕(wp )/⋕w (2) p (negative∖w) =(P(negative⋂w))/(p(w)) =⋕(wN )/⋕w(3) …”