In recent years people have become increasingly reliant on social media to read news and get information, and some social media users post unsubstantiated information to gain attention. Such information is known as rumours. Nowadays, rumour detection is receiving a growing amount of attention because of the pandemic of the New Coronavirus, which has led to a large number of rumours being spread. In this paper, a Natural Language Processing (NLP) system is built to predict rumours. The best model is applied to the COVID-19 tweets to conduct exploratory data analysis. The contribution of this study is twofold: (1) to compare rumours and facts using state-of-the-art natural language processing models in two dimensions: language structure and propagation route. (2) An analysis of how rumours differ from facts in terms of their lexical use and the emotions they imply. This study shows that linguistic structure is a better feature to distinguish rumours from facts compared to the propagation path. In addition, rumour tweets contain more vocabulary related to politics and negative emotions.

翻译：近年来，人们越来越依赖社交媒体阅读新闻和获取信息，部分社交媒体用户为博取关注而发布未经证实的信息，这类信息被称为谣言。随着新冠病毒大流行导致大量谣言传播，谣言检测正受到越来越多的关注。本文构建了一个自然语言处理（NLP）系统用于预测谣言，并将最佳模型应用于COVID-19推文以进行探索性数据分析。本研究的贡献有两方面：（1）从语言结构和传播路径两个维度，利用最先进的自然语言处理模型比较谣言与事实；（2）分析谣言与事实在词汇使用和隐含情感上的差异。研究表明，相较于传播路径，语言结构是区分谣言与事实的更优特征。此外，谣言推文包含更多与政治和负面情感相关的词汇。