Identifying Fake News Using Long-Short Term Memory Model - Farhan Wundari; Muhammad Nathan Asy Syaiba Amien; Dida Haiman Irtsa (Indonesian Journal of Data Science, IoT, Machine Learning and Informatics, 2024)
Designed to deceive readers and manipulate public opinion, fake news can be created for a variety of reasons ranging from political propaganda to generating revenue through clickbait. Another significant challenge in combating fake news is the difficult balance between curbing misinformation and preserving free speech, though some argue for stricter regulations to control the spread of fake news. Thus, the purpose of this study is to identify fake news using Long-Short Term Memory (LSTM). LSTM models are often used to analyze the linguistic features of news articles or social media posts. The dataset we used comes from a dataset of fake news on Kaggle's website. The proposed method can identify fake news with average precision, recall, accuracy, and f-measure values of 0.94, 0.96, 0.94, and 0.95. The results showed that LSTM provides superior performance compared to the Support Vector Classifier, Logistic Regression, and Multinomial Naive Bayes methods.