creator Chen, Pin-Chien 2024 Spring 2024 Thesis Illinois Institute of Technology ECE / Electrical and Computer Engineering advisor Cheng, Yu Electrical engineering Data Augmentation DistilBERT Fake News Large Language Models Machine Learning Natural Language Processing en With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation. born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access