Comparative Analysis of TF–IDF and Hashing Vectorizer for Fake News Detection in Sindhi: A Machine Learning and Deep Learning Approach †

Abstract

Social media has become a popular platform for accessing and sharing news, but it has also led to a rise in fake news, posing serious risks. The ease of dissemination and constant flow of information raise concerns about the spread of incorrect information. Timely verification of news is crucial to combat false news. However, most research on false news identification has focused on English, neglecting South Asian languages. This study examines a dataset of Sindhi tweets, employing text feature extraction techniques such as TF–IDF and hashing vectorizer. Several machine learning algorithms, along with advanced deep learning models such as Transformer BERT, were utilized for analysis.