Online reviews have a substantial impact on decision making in various areas of society, predominantly in the arena of buying and selling of goods. As such, the truthfulness of internet reviews is critical for both consumers and vendors. Fake reviews not only mislead innocent clients and influence customers' choice, leading to inaccurate descriptions and sales. This raises the need for efficient fake review detection models and tools that can address these issues. Analysing a text data stream of fake reviews in concept drift appears to reduce the effectiveness of the detection models. Despite several efforts to develop algorithms for detecting fake reviews, one crucial aspect that has not been addressed is finding a real correlation between the concept drift score and the classification of performance over-time in the real-world data stream. Consequently, we have introduced a comprehensive analysis to investigate the concept drift problem within fake review detection. There are two methods to achieve this goal: benchmarking concept drift detection method and content-based classification methods. We conducted our experiment using four real-world datasets from Yelp.com. The results demonstrated that there is a strong negative correlation between concept drift and the performance of fake review detection/prediction models, which indicates the difficulty of building more efficient models.

在线评论对社会各个领域的决策产生重大影响，主要是在商品买卖领域。因此，互联网评论的真实性对于消费者和供应商都至关重要。虚假评论不仅会误导无辜的客户并影响客户的选择，从而导致描述和销售不准确。这就需要能够解决这些问题的有效的假评论审查模型和工具。分析概念漂移中的假评论的文本数据流似乎降低了检测模型的有效性。尽管开发了用于检测伪造评论的算法的各种努力，但尚未解决的一个关键方面是在真实数据流中找到概念漂移评分与长时间性能分类之间的真实关联。因此，我们引入了一个综合分析来调查假评论检测中的概念漂移问题。有两种方法可以实现此目标：基准测试概念漂移检测方法和基于内容的分类方法。我们使用来自Yelp.com的四个真实世界数据集进行了实验。结果表明，概念漂移与伪造评论检测/预测模型的性能之间存在很强的负相关性，这表明构建更有效模型的难度很大。我们使用来自Yelp.com的四个真实世界数据集进行了实验。结果表明，概念漂移与伪造评论检测/预测模型的性能之间存在很强的负相关性，这表明构建更有效模型的难度很大。我们使用来自Yelp.com的四个真实世界数据集进行了实验。结果表明，概念漂移与伪造评论检测/预测模型的性能之间存在很强的负相关性，这表明构建更有效模型的难度很大。