| dc.description.abstract |
The exponential growth of social media has yielded
several advantages, but it has also brought about a major challenge
in the form of “fake news”, which has become a substantial
hindrance to journalism, freedom of expression, and democracy
at large. The purpose of this study was to examine the current
AI techniques employed for detecting fake news, determine
their limitations, and compare them with the latest models. The
performance of memory-based and Ensemble methods (LSTM,
Bi-LSTM, BERT, Distilled BERT, XGBoost, and AdaBoost) was
compared with traditional methods, and the impact of ensemble
learning was evaluated. The study aimed to identify appropriate
models for fake news detection in order to facilitate a secure
and reliable environment for information sharing on social media
and ultimately counteract the spread of false information. |
en_US |