论文标题
使用图形神经网络和NLP技术在社交媒体中的假新闻检测：covid-19
Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case
论文作者
论文摘要
该论文介绍了我们针对中世纪2020年任务的解决方案，即Fakenews：Corona病毒和5G阴谋多媒体Twitter-Data分析。该任务旨在分析与COVID-19和5G阴谋论有关的推文，以检测错误信息传播器。该任务由两个子任务组成，即（i）基于文本的和（ii）基于结构的假新闻检测。对于第一个任务，我们提出了六种不同的解决方案，依靠单词（弓）和伯特嵌入。其中三种方法旨在通过区分5G阴谋和COVID-19相关的推文来旨在二进制分类任务，而其余的推文则将任务视为三元分类问题。在三元分类任务中，我们的基于BOW和BERT的方法的F1得分分别为.606％和.566％的开发集。在二进制分类上，基于弓和BERT的解决方案的平均F1得分分别为.666％和.693％。另一方面，对于基于结构的假新闻检测，我们依靠图形神经网络（GNNS）在开发集中的平均ROC为0.95％。
The paper presents our solutions for the MediaEval 2020 task namely FakeNews: Corona Virus and 5G Conspiracy Multimedia Twitter-Data-Based Analysis. The task aims to analyze tweets related to COVID-19 and 5G conspiracy theories to detect misinformation spreaders. The task is composed of two sub-tasks namely (i) text-based, and (ii) structure-based fake news detection. For the first task, we propose six different solutions relying on Bag of Words (BoW) and BERT embedding. Three of the methods aim at binary classification task by differentiating in 5G conspiracy and the rest of the COVID-19 related tweets while the rest of them treat the task as ternary classification problem. In the ternary classification task, our BoW and BERT based methods obtained an F1-score of .606% and .566% on the development set, respectively. On the binary classification, the BoW and BERT based solutions obtained an average F1-score of .666% and .693%, respectively. On the other hand, for structure-based fake news detection, we rely on Graph Neural Networks (GNNs) achieving an average ROC of .95% on the development set.
