## 1 Introduction
A few studies have used embeddings from language model (ELMO), such as Bidirectional Encoder Representations from Transformers (BERT), that have outperformed several baseline methods in fake news detection [25; 26]. BERT employs semi-supervised learning and a language representation model that only uses the transformer's encoder [27]. However, these DL-based approaches are language-dependent and mostly rely on text-based features of English news [7]. A study by [PERSON] and [PERSON] [28] proposed easy data augmentation (EDA) techniques for boosting performance on text classification tasks. EDA consists of four simple yet powerful operations: synonym replacement, random insertion, random swap, and random deletion. Another study by [PERSON] et al. [29] proposed three text augmentation methods: synonym replacement, warping the tokens of words, and language generation with RNN. Text generation is performed by [PERSON] et al. [30] with sequence GAN. However, these methods only work well for short texts. Bengali is the world's seventh most popular language. Two hundred and sixty-eight million people speak this language in Bangladesh and parts of India [31]. Thanks to the increase in the use of electronic devices, digital Bengali text content has increased dramatically in recent years on the internet and news portals. However, scientific investigation into Bengali Language Processing (BLP) remains in its early stages [32]. A study by Al-Zaman [33] conducted a study that examined 419 bogus news articles published in India, a country prone to fake news, to identify the primary themes, types of content, and sources of fake news on social media. Fake news circulating on social media focuses on six main topics: health, politics, crime, entertainment, and miscellaneous. Health-related fake news is more prevalent during health crises, such as when misleading online news about COVID-19 endangered the health of Indians [33]. In contrast, fake news about politics and religion is exceptionally prevalent and originates from online media [34]. Moreover, a lack of benchmark datasets of Bengali fake news is a significant issue in this research area. The imbalanced data on the accessible dataset is another significant problem in this field. A text augmentation strategy is required for this purpose, as manual labeling of news as fake or true is time-consuming. Because the BERT model was initially trained using the English language, it is unsatisfactory when utilized to classify Bengali news using transfer learning. Existing research on the Bengali language has mainly used pre-trained BERT for Bengali news classification, which has not yielded satisfactory results [35]. As a result, a system that augments the text and trains the BERT model from scratch may work well for Bengali. We determined that an effective fake news detection strategy with sufficient Bengali text is required. Therefore, we propose a classification mechanism with text augmentation that can classify Bengali news as fake or true. This model can be used by anyone willing to incorporate artificial intelligence into their systems to detect fake news. The system can help social media users differentiate between fake and true news. As a result, users can avoid being misled by the spread of fake news. The overall contribution of this paper includes:  
* We propose AugFake-BERT, which integrates data augmentation with transfer learning to generate synthetic texts to supplement our fake news dataset. This study is the first to use a transfer learning approach to deal with imbalanced text data. * We trained a miniature BERT model from scratch with our original and augmented dataset to extract contextual features and perform the classification with a softmax layer. * We experimented with various BERT parameters to find the best-fit parameters for the Bengali fake news data. * Different baseline machine learning (ML) and deep learning (DL) models were used to evaluate the proposed strategy.