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

社交媒体通过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。假新闻巧妙地结合了可证实的事实和骇人听闻的虚构，旨在吸引读者的注意，产生立竿见影的影响，但很快就会被遗忘。即使作为一次性的新鲜事，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具有挑战性的，因为它们的复杂结构，用事实伪装小说，以及通过纳入最新和最热门的话题来不断进化。Live Reality项目将通过不断从权威来源输入可靠的、经过核实的事实来跟踪假新闻的演变，并将事实与虚构分开，以便在行动中捕捉假新闻。与由人工标记的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，传统的ML分类器仅限于在事实发生后很久才能找到历史假新闻。考虑到一次性新奇信息的生命周期较短(几天或几个小时)，在行为中捕捉假新闻需要在两个维度上进行重大创新。首先，ML分类器必须不断更新，以识别以前从未见过的真正的新奇事物。其次，更新必须足够及时，以便在一次性新奇物品过期之前发现它们，例如在它们最初传播后的几个小时内。持续收集实时社交媒体和权威来源将生成新颖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组合分类器中，以区分假新闻中可验证的事实和虚构。随着新闻话题的演变，假新闻有望接踵而至，EBKA将生成新子模型并将其整合到直播组合分类器中，以识别新话题。EBKA方法将在包含各种主题假新闻的现场数据上进行演示，特别是灾难管理，如新冠肺炎大流行。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确度方面的表现，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及时性。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.