EAGER: Live Reality: Sustainable and Up-to-Date Information Quality in Live Social Media through Continuous Evidence-Based Knowledge Acquisition

社交媒体以即时报道和全球报道补充了传统媒体。然而，他们也接收和传播大量的错误信息和虚假信息，如假新闻。假新闻巧妙地混合了可证实的事实和离谱的虚构，旨在吸引读者的注意力，立即产生初步影响，然后很快被遗忘。即使是一次性的新鲜事物，假新闻也对选举等现实世界的事件产生了重大影响。对于人类读者和机器学习（ML）分类器来说，区分假新闻和真实新闻一直是一项挑战，因为它们的结构复杂，用事实掩盖虚构，并且随着最新和最热门的话题的变化而不断发展。Live Reality项目将通过不断从权威来源导入可靠、经过验证的事实，追踪假新闻的演变，并将事实与虚构分开，在行动中抓住假新闻。与人工标记训练数据生成的传统ML分类器相比，自动实时跟踪能力是一项重大创新，传统ML分类器只能在事实发生很久之后发现历史假新闻。鉴于一次性新鲜事物的寿命很短（几天或几小时），在行动中捕捉假新闻需要在两个维度上进行重大创新。首先，机器学习分类器必须不断更新，以识别以前从未见过的真正的新颖性。其次，更新必须足够及时，以便在一次性新产品过期之前抓住它们，例如，在它们最初传播的几个小时内。持续收集实时社交媒体和权威来源将产生新的假新闻和相关的地面真相，并通过基于证据的知识获取（EBKA）方法进行整合，该方法将来自权威来源的可靠信息添加到持续自适应的团队分类器中，以区分假新闻中的可验证事实和虚构。随着新闻主题的演变，假新闻也会随之出现，EBKA将生成并集成新的子模型到实时团队分类器中，以识别新主题。EBKA方法将在包含各种主题假新闻的实时数据上进行演示，特别是灾害管理，如COVID-19大流行。由于假新闻的一次性新颖性，EBKA将从两个维度进行评估：分类器在准确性和精度方面的性能，以及分类器在真实世界中出现后不久识别真正新的假新闻的时效性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Social media have complemented traditional press with immediate reports and worldwide coverage. However, they also receive and propagate significant amounts of misinformation and disinformation such as fake news. A skillful mixture of verifiable facts and outrageous fiction, fake news aim to attract reader attention, make an immediate initial impact, and then quickly forgotten. Even as disposable novelty, fake news have had significant impact on real world events such as elections. For human readers and machine learning (ML) classifiers, distinguishing fake news from real news has been challenging due to their sophisticated construction, camouflaging fiction with facts, as well as continuously evolving by incorporating the newest and hottest topics as they mutate. The Live Reality project will track the evolution of fake news through continuous import of reliable, verified facts from authoritative sources, and separate the facts from fiction, to catch fake news in the act. The automated real-time tracking capability is a significant innovation compared to traditional ML classifiers generated from manually labeled training data, which are constrained to finding historical fake news, long after the fact.Given the short lifespan of disposable novelty (days or hours), catching fake news in the act requires significant innovation in two dimensions. First, the ML classifier must be continuously updated to recognize true novelty that have never been seen before. Second, the update must be sufficiently timely to catch disposable novelty before they expire, e.g., within hours of their initial dissemination. Continuous collection of live social media and authoritative sources will generate novel fake news and associated ground truth, which are integrated through the Evidence-Based Knowledge Acquisition (EBKA) approach, which adds reliable information from authoritative sources into a continuously adaptive teamed classifier to distinguish the verifiable facts from the fiction in fake news. As news topics evolve, fake news are expected to follow, and EBKA will generate and integrate new sub-models into the live teamed classifier to recognize the new topics. The EBKA approach will be demonstrated on live data containing fake news on a variety of topics, specifically disaster management such as the COVID-19 pandemic. Due to the disposable novelty nature of fake news, EBKA will be evaluated in two dimensions: classifier performance in terms of accuracy and precision, and timeliness of classifier identifying truly new fake news soon after their appearance in the real world.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.