假新闻检测系统:BERT和Boosting算法的实现

Raquiba Sultana, T. Nishino
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引用次数: 0

摘要

在社交媒体上,虚假信息可以迅速扩散并引发大问题。为了最大限度地减少虚假信息造成的危害,有必要了解其敏感性和内容。要做到这一点,有必要首先确定信息的特征。为了识别互联网上的虚假信息,本文提出了一种基于变压器的集成模型。首先,进行各种文本分类任务,了解Covid-19假新闻和真实新闻的内容。提出的混合集成学习模型利用了这些结果。我们的分析结果令人鼓舞,表明建议的系统可以识别社交媒体上的虚假信息。对所有分类任务进行了验证,并取得了显著的效果。最终模型的准确率为0.99,F1分数为0.99。受试者工作特征(ROC)曲线显示,该模型中数据的真阳性率接近于1,曲线下面积(AUC)得分也非常高,为0.99。因此,该模型在识别在线虚假信息方面是有效的。
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Fake News Detection System: An implementation of BERT and Boosting Algorithm
On social media, false information can proliferate quickly and cause big issues. To minimize the harm caused by false information, it is essential to comprehend its sensitive nature and content. To achieve this, it is necessary to first identify the characteristics of information. To identify false information on the internet, we suggest an ensemble model based on transformers in this paper. First, various text classification tasks were carried out to understand the content of false and true news on Covid-19. The proposed hybrid ensemble learning model used the results. The results of our analysis were encouraging, demonstrating that the suggested system can identify false information on social media. All the classification tasks were validated and shows outstanding results. The final model showed excellent accuracy (0.99) and F1 score (0.99). The Receiver Operating Character- istics (ROC) curve showed that the true-positive rate of the data in this model was close to one, and the AUC (Area Under The Curve) score was also very high at 0.99. Thus, it was shown that the suggested model was effective at identifying false information online.
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ARCH-COMP23 Category Report: Hybrid Systems Theorem Proving ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Linear Continuous Dynamics ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics ARCH-COMP23 Repeatability Evaluation Report ARCH-COMP23 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants
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