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DamascusTeam at NLP4IF2021: Fighting the Arabic COVID-19 Infodemic on Twitter Using AraBERT

机译:NLP4IF2021的DAMASCUSTA:使用阿拉伯语在推特上与阿拉伯COVID-19信息系统作战

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In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of infodemic misinformation at an incredible pace and scale. Consequently, the research on the infodemic of the post's misinformation is becoming more important than ever before. In this paper, we present our approach using AraBERT (Trans former-based Model for Arabic Language Understanding) to predict 7 binary properties of an Arabic tweet about COVID-19. To train our classification models, we use the dataset provided by NLP4IF 2021. We ranked 5th in the Fighting the COVID-19 Infodemic task results with an F1 of 0.664.
机译:在现代计算机时代,新闻生态系统已经从旧的传统印刷媒体转变为社交媒体。社交媒体平台让我们能够更快地消费新闻,更少的编辑结果限制了信息传播的速度和规模。因此,对《邮报》虚假信息的信息学研究变得比以往任何时候都重要。在本文中,2019冠状病毒疾病的预测,我们提出了使用阿拉伯特(基于阿拉伯语的基于语言的阿拉伯语理解模型)来预测COVID-19的7个二进制属性的方法。为了训练我们的分类模型,我们使用由NLP4IF 2021提供的数据集。在2019冠状病毒疾病的任务中,我们排名第五,F1的任务结果为0.664。

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