情感LIAR：用于虚假索赔分类的扩展语料库和深度学习模型
Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification
摘要：
社交媒体在我们日常生活和文化中的广泛融合，使人们比人类历史上任何时候都更容易快速地获得信息流。然而，社交媒体平台固有的不受监督的性质也使虚假信息和假新闻更容易传播。此外，此类平台中信息流的高容量和速度使得人工监督和控制信息传播不可行。本文旨在通过提出一种新的深度学习方法来自动检测社交媒体上的虚假短文本声明来解决这个问题。我们首先介绍Sentimental LIAR，它通过添加基于索赔的情感和情绪分析的特征来扩展空头索赔的LIAR数据集。此外，我们提出了一种基于BERT-Base语言模型的新型深度学习架构，用于将索赔分类为真实或虚假。我们的研究结果表明，在情感LIAR上训练的建议架构可以达到70%的准确率，这比之前报道的LIAR基准测试结果提高了约30%。
英文摘要：
The rampant integration of social media in our every day lives and culture has given rise to fast and easier access to the flow of information than ever in human history. However, the inherently unsupervised nature of social media platforms has also made it easier to spread false information and fake news. Furthermore, the high volume and velocity of information flow in such platforms make manual supervision and control of information propagation infeasible. This paper aims to address this issue by proposing a novel deep learning approach for automated detection of false short-text claims on social media. We first introduce Sentimental LIAR, which extends the LIAR dataset of short claims by adding features based on sentiment and emotion analysis of claims. Furthermore, we propose a novel deep learning architecture based on the BERT-Base language model for classification of claims as genuine or fake. Our results demonstrate that the proposed architecture trained on Sentimental LIAR can achieve an accuracy of 70%, which is an improvement of ~30% over previously reported results for the LIAR benchmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
