The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. This paper introduces the Bengali Fake Review Detection (BFRD) dataset, the first publicly available dataset for identifying fake reviews in Bengali. The dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. To convert non-Bengali words in a review, a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali. We have conducted rigorous experimentation using multiple deep learning and pre-trained transformer language models to develop a reliable detection system. Finally, we propose a weighted ensemble model that combines four pre-trained transformers: BanglaBERT, BanglaBERT Base, BanglaBERT Large, and BanglaBERT Generator . According to the experiment results, the proposed ensemble model obtained a weighted F1-score of 0.9843 on 13390 reviews, including 1339 actual fake reviews and 5356 augmented fake reviews generated with the nlpaug library. The remaining 6695 reviews were randomly selected from the 7710 non-fake instances. The model achieved a 0.9558 weighted F1-score when the fake reviews were augmented using the bnaug library.

翻译：在线平台上虚假评论的泛滥已对消费者和企业构成重大关切。这类评论可能欺骗顾客，损害产品或服务的声誉，因此识别它们至关重要。尽管英语虚假评论检测已得到广泛研究，但在孟加拉语等非英语语言中检测虚假评论仍是一个相对未被探索的研究领域。本文介绍了孟加拉语虚假评论检测（BFRD）数据集，这是首个公开可用于识别孟加拉语虚假评论的数据集。该数据集包含从社交媒体帖子收集的7710条非虚假评论和1339条虚假食品相关评论。为转换评论中的非孟加拉语词汇，我们提出了一种独特流程，可将英语单词翻译为对应的孟加拉语释义，并将罗马化的孟加拉语反向音译回孟加拉语。我们使用多种深度学习模型和预训练Transformer语言模型进行了严格实验，以构建可靠的检测系统。最后，我们提出了一种加权集成模型，融合了四种预训练Transformer：BanglaBERT、BanglaBERT Base、BanglaBERT Large和BanglaBERT Generator。实验结果表明，所提出的集成模型在13390条评论（含1339条实际虚假评论和5356条使用nlpaug库增强生成的虚假评论）上取得了0.9843的加权F1分数；剩余6695条评论从7710条非虚假样本中随机抽取。当使用bnaug库对虚假评论进行增强时，模型取得了0.9558的加权F1分数。