Collaborative Research: CISE-MSI: DP: IIS: Event Detection and Knowledge Extraction via Learning and Causality Analysis for Resilience Emergency Response

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。该项目利用从社交媒体收集的关于即将发生事件的信息，及时通知指定当局，以便他们在紧急情况下制定缓解行动计划。除了提取的事件本身，收集的信息可能包括（但不限于）图像、发布的消息、人们的情绪和其他周围环境，这将提高信息的相关性和可信度，有助于了解紧急情况。提取的事件成为调查和分析事件之间的时空影响和跨域事件的来源，以获得进一步的见解。潜在的应用是实时跟踪和监测救灾事件，以及预测减灾计划中的事件。项目成果将有利于研究人员在信息提取和集成与兴趣图模型和迁移学习；除了提供深度学习、时空数据因果关系和分析等领域的实用学习材料外，还为学生提供有关灾害恢复能力和长期社区恢复能力的进展。此外，这项工作将增加研究能力和合作，为来自代表性不足的社区的学生提供新的研究机会，以攻读计算机科学的高级学位。社交媒体数据提供了一种方法来识别灾难发生之前、期间和之后发生的事件。它为制定反应和减灾规划的有关当局提供信号。本研究将在三个方面使用社交媒体帖子、机器学习和迁移学习技术：1)提取本地和全球事件；2)嵌入周边情境，如相关性和信任；3)分析事件和跨域事件之间的时空关系，以获得进一步的见解。该项目利用深度学习的最新进展和机会，在图神经网络和迁移学习的保护下提出了一种新的事件分析方法。将对由此产生的数据驱动算法进行建模，通过允许系统根据基于社区的变量和灾害的动态进行调整，强调后果和级联损失的社会经济方面。研究结果将通过出版物、源代码和数据传播给不同的研究人员和学生群体。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This project utilizes information gleaned from social media about upcoming events to inform designated authorities in a timely manner so they can prepare mitigating action plans in case of emergency. Besides the extracted events themselves, harvested information may include (but is not limited to) images, posted messages, people’s sentiments and other surrounding context which will improve relevancy and trust of the information in understanding emergency situations. Extracted events become the source for investigating and analyzing spatial-temporal influences between events and cross-domain events to derive further insights. Potential applications are real-time tracking and monitoring of events for disaster relief, and forecasting of events for mitigation plans. Project outcomes will benefit researchers in information extraction and integration with interests in graph models and transfer learning; in addition to providing practical studying materials in areas such as deep learning, spatio-temporal data causality and analysis for students about disaster resilience and progressing towards community resilience in the long term. Moreover, the work will increase research capacity and collaborations to generate new research opportunities for students from underrepresented communities to pursue advanced degrees in computer science. Social media data provides a means to identify happening events prior, during, and post disasters. It provides signals for designed authorities for reactions and mitigation planning. This research will use social media posts, machine learning, and transfer learning techniques in three thrusts: 1) Extract local and global events; 2) Embed surrounding context such as relevance and trust; 3) Analyze spatial-temporal relationship between events and cross-domain events for further insights. This project puts forth a novel approach to events analysis under the umbrella of graph neural network and transfer learning, leveraging recent advances and opportunities in deep learning. The resulting data-driven algorithms will be modelled emphasizing the socio-economic aspects of the consequences and cascading losses by allowing the system to adapt according to the community-based variables and the dynamics of the disasters. The findings will be disseminated via publications, source code, and data to reach diverse communities of researchers and students.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.