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首页> 外文会议>IEEE International Conference on Cognitive Machine Intelligence >Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media
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Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media

机译：快速流行信息传播与社交媒体的现场知识聚合的挑战和机遇

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A rapidly evolving situation such as the COVID-19 pandemic is a significant challenge for AI/ML models because of its unpredictability. The most reliable indicator of the pandemic spreading has been the number of test positive cases. However, the tests are both incomplete (due to untested asymptomatic cases) and late (due the lag from the initial contact event, worsening symptoms, and test results). Social media can complement physical test data due to faster and higher coverage, but they present a different challenge: significant amounts of noise, misinformation and disinformation. We believe that social media can become good indicators of pandemic, provided two conditions are met. The first (True Novelty) is the capture of new, previously unknown, information from unpredictably evolving situations. The second (Fact vs. Fiction) is the distinction of verifiable facts from misinformation and disinformation. Social media information that satisfy those two conditions are called live knowledge. We apply evidence-based knowledge acquisition (EBKA) approach to collect, filter, and update live knowledge through the integration of social media sources with authoritative sources. Although limited in quantity, the reliable training data from authoritative sources enable the filtering of misinformation as well as capturing truly new information. We describe the EDNA/LITMUS tools that implement EBKA, integrating social media such as Twitter and Facebook with authoritative sources such as WHO and CDC, creating and updating live knowledge on the COVID-19 pandemic.
机译：由于其不可预测性，Covid-19 Pandemic在Covid-19大流行之类的迅速发展的情况是AI / ML模型的重大挑战。大流行蔓延的最可靠指标一直是测试阳性案例的数量。然而，测试既不完整）（由于未经测试的无症状病例）和晚期（由于初始接触事件的滞后，症状恶化和测试结果）。社交媒体可以通过更快和更高的覆盖率来补充物理测试数据，但它们存在不同的挑战：大量的噪音，错误信息和消毒。我们相信社交媒体可以成为大流行的良好指标，提供了两个条件。第一个（真正的新颖性）是捕获来自不可预测地发展情况的新的，以前未知的信息。第二个（事实与小说）是核实信息与误导性和禁令的区别。满足这两个条件的社交媒体信息称为现场知识。我们通过与权威来源集成社交媒体来源，应用基于证据的知识获取（EBKA）方法来收集，过滤和更新现场知识。虽然数量有限，但权威来源的可靠培训数据能够过滤错误信息以及捕获真正的新信息。我们描述了实现EBKA的EDNA / Litmus工具，将社交媒体（如Twitter和Facebook）与WHO和CDC等权威来源相结合，创建和更新Covid-19大流行的实况知识。

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