A Benchmark of Machine Learning and Deep Learning Algorithms for Detecting Fake News in Bangla Language

EasyChair Preprint no. 9190

Abstract

Due to the ease with which information may be obtained and the exponential growth in the amount of information available on the internet, it has become more challenging to differentiate between false and genuine information. Any of these fake news websites may easily infect people due to their fabricated claims. This situation has a significant impact on the offline community in general. As a result, interest in this subject has grown. A critical study has been conducted on identifying fake news in English and other languages, save for a few in Bangla. Our research shows an experimental benchmark investigation in identifying false news on a Bengali news website since there is less work in this domain. This research analyses 11434 fake news and true news in the Bengali language and evaluates the performance of machine learning and deep learning algorithms to create a benchmark for detecting Bangla fake news. This research compares the model’s performance to a variety of linguistic characteristics and word vectorizers. The best accuracy obtained for lemmatized text is 95.45% for TF-IDF with the SGD classifier and 95.10% for the count vectorizer with the MLP classifier. For stemmed text, we received the best accuracy of 94.9% for the Count Vectorizer with MLP classifier and 94.83% for TF-IDF with MLP classifier. Among deep learning models, RNN gave the best performance with 96.55% accuracy where the f1 score is 0.96. The pre-trained Bangla BERT model gave an F1-Score of 0.96 and showed an accuracy of 93.35%.

Keyphrases: Bangla BERT Model, Bangla Fake News, Benchmark Analysis, Count Vectorizer, Deep Learning Algorithms, Fake News Detection, Machine Learning Algorithms, NLP, RNN, TF-IDF, word2vec

@Booklet{EasyChair:9190,
  author = {Nirjas Mohammad Jakilim and Sm Mahamudul Hasan and Enamul Hassan},
  title = {A Benchmark of Machine Learning and Deep Learning Algorithms for Detecting Fake News in Bangla Language},
  howpublished = {EasyChair Preprint no. 9190},

  year = {EasyChair, 2022}}