A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects

Abstract

In the present era, social media platforms such as Facebook, WhatsApp, Twitter, and Telegram are significant sources of information distribution, and people believe it without knowing their origin and genuineness. Social media has fascinated people worldwide in spreading fake news due to its easy availability, cost-effectiveness, and ease of information sharing. Fake news can be generated to mislead the community for personal or commercial gains. It can also be used for other personal benefits such as defaming eminent personalities, amendment of government policies, etc. Thus, to mitigate the awful consequences of fake news, several research types have been conducted for its detection with high accuracy to prevent its fatal outcome. Motivated by the aforementioned concerns, we present a comprehensive survey of the existing fake news identification techniques in this paper. Then, we select Machine Learning (ML) models such as Long-Short Term Memory (LSTM), Passive Aggressive Algorithm, Random Forest (RF), and Naive Bayes (NB) and train them to detect fake news articles on the self-aggregated dataset. Later, we implemented these models by hyper tuning various parameters such as smoothing, drop out factor, and batch size, which has shown promising results in accuracy and other evaluation metrics such as F1-score, recall, precision, and AUC score. The model is trained on 6335 news articles, with LSTM showing the highest accuracy of 92.34% in predicting fake news and NB were showing the highest recall. Based on these results, we propose a hybrid fake news detection technique using NB and LSTM. At last, challenges and open issues along with future research directions are discussed to facilitate the research in this domain further.

News classification using light gradient boosted machine algorithm

“…LSTM achieved an average accuracy of 94.21%. Similarly, Rohera et al [12] also shows LSTM outperform SVM, random forests (RF), and SVM in classifying fake news. On the other side, [13] found that XGBoost (XGB) and RF performed best in detecting fake news among k-nearest neighbors (KNN), NB, RF, SVM with RBF kernel (SVM), and XGB.…”

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society

“…72 [123] A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects 73 [124] The Impact of the COVID- [134] Public perception of SARS-CoV-2 vaccinations on social media: Questionnaire and sentiment analysis 84 [135] Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization 85 [136] Cultural Evolution and Digital Media: Diffusion of Fake News About COVID-19 on Twitter 86 [137] Covid-19 vaccine hesitancy on social media: Building a public twitter data set of antivaccine content, vaccine misinformation, and conspiracies 87 [138] News media stories about cancer on Facebook: How does story framing influence response framing, tone and attributions of responsibility?…”

ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction

“…Classification involves grouping entities based on a set of ordered features. In this work, 3D convolutional neural networks (3D CNNs) [ 50 , 51 , 52 ] were employed to classify tumors into LGG and HGG categories. The 3D CNNs have successive convolution layers and rectified linear unit (ReLU) functions.…”