Learning from COVID-19: An AI-enabled evidence-driven framework for claim veracity assessment during pandemics

世界卫生组织创造的“信息者”一词指的是大流行期间的错误信息，可能会造成恐慌，破坏社会反应，影响传播率；鼓励未经测试的治疗方法的贸易，使人们的生命处于危险之中。世界卫生组织和政府机构必须转移大量资源来对抗信息症。它们的规模使得使用计算技术来评估索赔的准确性变得至关重要。然而，现有的方法在很大程度上依赖于监督学习。目前的准确性水平低于实际采用所需的水平，因为培训数据很小，而且对于培训期间看不到的索赔/主题，性能往往会大幅下降：当前的做法不适合处理新冠肺炎信息的规模和复杂性。该项目将研究新的监督/非监督方法，通过整合来自多个来源的信息和建立一个能够交叉核实的知识网络，评估在邮寄时未经核实的索赔的准确性。将通过错误信息传播的模式识别关键的来源/代理，并通过一个新的可视化界面显示结果，以便于用户解释。这一高水平的目标产生了以下目标：RO1。从社交媒体平台和权威资源收集新冠肺炎相关数据。开发自动化方法，从科学出版物和其他相关来源提取新冠肺炎上的关键信息。通过吸收外部来源的证据，开发新的无监督/监督的准确性评估方法。RO4.分析谣言在社交媒体中的动态传播模式；确定关键来源/代理人，并制定有效的遏制策略。RO5。通过一组新的可视化界面验证方法。

The term 'infodemic' coined by the WHO refers to misinformation during pandemics that can create panic, fragment social response, affect rates of transmission; encourage trade in untested treatments that put people's lives in danger. The WHO and government agencies have to divert significant resources to combat infodemics. Their scale makes it essential to employ computational techniques for claim veracity assessment. However, existing approaches largely rely on supervised learning. Present accuracy levels fall short of that required for practical adoption as training data is small and performance tends to degrade significantly on claims/topics unseen during training: current practices are unsuitable for addressing the scale and complexity of the COVID-19 infodemic. This project will research novel supervised/unsupervised methods for veracity assessment of claims unverified at the time of posting, by integrating information from multiple sources and building a knowledge network that enables cross verification. Key originating sources/agents will be identified through patterns of misinformation propagation and results will be presented via a novel visualisation interface for easy interpretation by users. This high-level aim gives rise to the following objectives: RO1. Collect COVID-19 related data from social media platforms and authoritative resources.RO2. Develop automated methods to extract key information on COVID-19 from scientific publications and other relevant sources.RO3. Develop novel unsupervised/supervised approaches for veracity assessment by incorporating evidence from external sources. RO4. Analyse dynamic spreading-patterns of rumour in social media; identify the key sources/agents and develop effective containment strategies. RO5. Validate the methods via a set of new visualisation interfaces.