论文标题
情感骗子：伪造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
论文作者
论文摘要
社交媒体在我们每天的生活和文化中的猖ramp融合使人们比人类历史上的信息流更加容易获取信息流。但是，社交媒体平台的固有无监督性质也使传播虚假信息和虚假新闻变得更加容易。此外，此类平台中信息流的大量和速度使手动监督和控制信息传播是不可行的。本文旨在通过提出一种新颖的深度学习方法来解决这个问题，以自动检测社交媒体上的虚假短文主张。我们首先介绍情感骗子，该骗子通过添加基于情感和情感分析的特征来扩展简短索赔的骗子数据集。此外，我们提出了一种基于Bert-Base语言模型的新型深度学习体系结构，以将索赔分类为真实或假货。我们的结果表明，对感性骗子训练的拟议的建筑可以达到70％的准确性，这比以前报道的骗子基准的结果提高了约30％。
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.
