论文标题
高影响事件期间可信赖的网络空间的独立组件分析：Covid-19的应用
Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
论文作者
论文摘要
社交媒体已成为高影响事件期间的重要沟通渠道，例如COVID-19大流行。由于社交媒体中的错误信息可以迅速传播，造成社交动荡，因此在此类事件中减少错误信息的传播是一个重大的数据挑战。尽管基于机器学习的最新解决方案已经显示出对检测错误信息的希望，但大多数广泛使用的方法包括依赖于无法对所有情况最佳的手工制作的特征或基于深度学习的那些手工制作的功能的方法，而对预测结果的解释是无法直接访问的。在这项工作中，我们提出了一个基于ICA模型的数据驱动解决方案，以便共同实现知识发现和检测错误信息。为了证明我们方法的有效性并通过深度学习方法比较其性能，我们根据社会语言标准开发了一个标记为Covid-19 Twitter数据集。
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation during such events is a significant data challenge. While recent solutions that are based on machine learning have shown promise for the detection of misinformation, most widely used methods include approaches that rely on either handcrafted features that cannot be optimal for all scenarios, or those that are based on deep learning where the interpretation of the prediction results is not directly accessible. In this work, we propose a data-driven solution that is based on the ICA model, such that knowledge discovery and detection of misinformation are achieved jointly. To demonstrate the effectiveness of our method and compare its performance with deep learning methods, we developed a labeled COVID-19 Twitter dataset based on socio-linguistic criteria.
