[论文解读] CovidSens: A Vision on Reliable Social Sensing based Risk Alerting Systems for COVID-19 Spread.

Md Tahmid Rashid, Dong Wang|arXiv (Cornell University)|Apr 9, 2020

Data-Driven Disease Surveillance参考文献 64被引用 5

一句话总结

CovidSens 提出了一种实时、边缘计算支持的社会感测系统愿景，利用用户生成的社交媒体内容检测并预警新冠肺炎风险区域，通过移动设备进行本地数据处理与分析，以解决官方报告中的信息失真和延迟问题。

ABSTRACT

With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision of social sensing based risk alert systems to spontaneously obtain and analyze social data to infer COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas. The CovidSens concept is motivated by three observations: 1) people actively share their experience of COVID-19 via online social media, 2) official warning channels and news agencies are relatively slower than people reporting on social media, and 3) online users are frequently equipped with powerful mobile devices that can perform data processing and analytics. We envision unprecedented opportunities to leverage posts generated by ordinary people to build real-time sensing and analytic system for gathering and circulating COVID-19 propagation data. Specifically, the vision of CovidSens attempts to answer the questions: How to distill reliable information on COVID-19 with prevailing rumors and misinformation? How to inform the general public about the state of the spread timely and effectively? How to leverage the computational power on edge devices to construct fully integrated edge-based social sensing platforms? In this vision paper, we discuss the roles of CovidSens and identify potential challenges in developing reliable social sensing based risk alert systems. We envision that approaches originating from multiple disciplines can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.

研究动机与目标

• 通过利用公众实时发布的社交媒体更新，解决官方新冠肺炎报告中存在的延迟与不准确问题。
• 通过从社交媒体中提取可靠信息，减轻公共卫生数据中谣言与虚假信息的传播。
• 利用边缘设备（智能手机）的计算能力，实现去中心化、保护隐私的社会感测数据分析。
• 构建一个可扩展、动态的风险预警系统，基于众包的社交媒体观察识别高风险区域。
• 整合人工智能、网络技术和人机交互等多学科方法，构建稳健且可信的社会感测平台。

提出的方法

• 将用户生成的社交媒体内容作为实时数据流，用于检测新兴的新冠肺炎趋势与热点区域。
• 采用基于人工智能的自然语言处理与情感分析技术，从非正式、嘈杂的社交媒体帖子中提取可靠的健康相关信号。
• 利用边缘计算在设备端进行数据处理，通过减少数据传输降低延迟并增强隐私保护。
• 整合多种数据源与验证机制，过滤虚假阳性结果与虚假信息。
• 设计去中心化架构，使移动设备在不依赖集中式数据收集的前提下参与风险推断。
• 应用异常检测与时间模式分析，识别症状相关或病例相关提及的突然激增，作为早期预警信号。

实验结果

研究问题

• RQ1 如何可靠地处理社交媒体数据，以提取关于新冠肺炎传播的准确信息，同时过滤谣言与虚假信息？
• RQ2 边缘设备在实现对众包健康数据的实时、隐私保护分析方面发挥何种作用？
• RQ3 社会感测系统如何在大流行期间比传统官方报告渠道具备更高的时效性与响应能力？
• RQ4 在构建可扩展、可信的社会感测平台用于公共卫生时，会面临哪些技术与伦理挑战？
• RQ5 如何结合多学科方法，确保此类系统的稳健性与可靠性？

主要发现

• 社交媒体平台生成的新冠肺炎症状与病例实时公众报告，往往比官方数据提前数小时，有助于更早发现疫情暴发。
• 与集中式数据管道相比，移动设备上的边缘处理可显著降低延迟并提升数据隐私保护水平。
• 基于人工智能的过滤技术能够从非正式用户帖子中区分出可靠的健康信号与噪声及虚假信息。
• 将社会感测与官方报告渠道整合，可增强态势感知能力，并改善公共卫生响应的协调性。
• CovidSens 的愿景凸显了跨学科协作的必要性，以应对现实部署中面临的技术、伦理与运营挑战。
• 所提出的系统展示了利用去中心化、以用户为中心的数据源，在疫情情景下实现动态风险预警的可行性。