Gender Bias in Online News Articles of a Computational Analysis Using AI-Based Critical Discourse Analysis

Various and accurate data processing and analysis have been made possible by computers for many years. In order to examine societal behavior by finding patterns in data, humanities and social science researchers are increasingly using computer technologies like artificial intelligence (AI) and machine learning (ML). Specifically, speculative sentences or clauses are frequently used in news item headlines and abstracts to draw in unwary readers. The dominance of men in the news is highly apparent, and Females are not adequately represented and underexamined in the same news categories as their male coworkers because they are seen as “eye candy” or “inferior” and are thus thought to be so. In this work, gender bias in online news items from two important English news datasets that were used for news classification and recommendation is explored. Additionally, the research discusses views of gender bias in news generated by AI. Through the use of a questionnaire with five different themes, information has been collected from 490 respondents. The data was analysed using support vector machines (SVM), decision trees (DT), random forests (RF), and artificial neural networks (ANN). The experimental results show that the performance of the AI algorithms is influenced by the theme-dependent prediction of gender perceptions in news stories. According to the experiment's findings, ANN consistently shows the most efficacy, with an accuracy level of 88%. This is followed by RF and SVM, which have accuracy levels of 85% and 81%, respectively. This work makes a substantial contribution to the subject by using AI to model behavior among people in civilization.