A Survey on Bias Detection in Online News using Deep Learning

Abstract

The detection of bias in online news has become a critical and sensitive area of research in recent years, largely due to the growing use of online platforms, such as social media, and the proliferation of news sources in digital format. This article provides a comprehensive review of existing studies on online bias detection using natural language processing, including an analysis of the methodologies employed, an overview of available datasets, and suggestions for further research in this field The article examines techniques such as data pre-processing, feature extraction, classification, and prediction in detail. Various deep learning algorithms, such as BERT and Long Short-Term Memory (LSTM), as well as machine learning algorithms, such as logistic regression, Recursive Neural Network models, and Naive Bayes, can be used to detect bias in news headlines and articles. The article concludes by discussing the potential impact of bias detection on journalism and society, as well as future research directions.