| Abstrakt: |
In order to identify and combat misinformation on social networking sites, the present study analyses the use of automated learning algorithms. With the appearance of the internet and its extensive use, social media structures have turn out to be massive venues for statistics dissemination and influence. But, these platforms have additionally been exploited for spreading propaganda and false data, concentrated on people, corporations, and political entities. Our study specializes in analysing diverse machine learning classifiers, which include Support Vector Machines (SVM), Random forest, and deep learning strategies like BERT and Roberta, to differentiate among propaganda and non-propaganda content. We make use of datasets collected from online information resources and Twitter, leveraging the Twitter API for information extraction. The paper presents a complete evaluate of cutting-edge methodologies, highlights the demanding situations faced in propaganda detection, and discusses the effectiveness of different machine learning models based on their accuracy and applicability in real-world eventualities. Our findings indicate that machine learning, especially advanced models like RoBERTa, can appreciably useful resource in figuring out and mitigating the spread of propaganda and extremist content on social networks. [ABSTRACT FROM AUTHOR] |
