BIGDATA: IA: Collaborative Research: Domain Adaptation Approaches for Classifying Crisis Related Data on Social Media

大数据：IA：协作研究：社交媒体上危机相关数据分类的领域适应方法

• 批准号: 1741353
• 负责人: Cornelia Caragea
• 金额: $ 40万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1741353&HistoricalAwards=false
• 关键词: BIGDATA; IA; Collaborative; Research; Domain

The project investigates the use of big-data analysis techniques for classifying crisis-related data in social media with respect to situational awareness categories, such as caution, advice, fatality, injury, and support, with the goal of helping emergency response teams identify useful information. A major challenge is the scale of the data, where millions of short messages are continuously posted during a disaster, and need to be analyzed. The use of current technologies based on automated machine learning is limited due to the lack of labeled data for an emergent target disaster, and the fact that every event is unique in terms of geography, culture, infrastructure, technology, and the people involved. To tackle the above challenges, domain adaptation techniques that make use of existing labeled data from prior disasters and unlabeled data from a current disaster are designed. The resulting models are continuously updated and improved based on feedback from crowdsourcing volunteers. The research will provide real, usable solutions to emergency response organizations and will enable these organizations to improve the speed, quality and efficiency of their response. The research provides novel solutions based on domain adaptation and deep neural networks to tackle the unique challenges in applying machine learning for crisis-related data analysis, specifically the volume and velocity challenges of big crisis data. Domain adaptation approaches enable the transfer of information from prior source disasters to an emergenet target disaster. Deep learning approaches make it possible to employ large amounts of labeled source data and unlabeled target data, and to incrementally update the models as more labeled target data becomes available. Large-scale analysis across combinations of source and target crises will help identify patterns of transferable situational awareness knowledge. The resulting technical and social solutions will be blended together for use in data management and emergency response.

该项目研究使用大数据分析技术对社交媒体中与危机相关的数据进行态势感知类别（例如警告、建议、死亡、伤害和支持）进行分类，目的是帮助应急响应团队识别有用的信息信息。一个主要挑战是数据规模，灾难期间会连续发布数百万条短信，需要进行分析。由于缺乏针对紧急目标灾难的标记数据，并且每个事件在地理、文化、基础设施、技术和相关人员方面都是独一无二的，基于自动化机器学习的当前技术的使用受到限制。为了应对上述挑战，设计了利用先前灾难的现有标记数据和当前灾难的未标记数据的领域适应技术。由此产生的模型会根据众包志愿者的反馈不断更新和改进。该研究将为应急响应组织提供真实、可用的解决方案，并使这些组织能够提高响应的速度、质量和效率。该研究提供了基于领域适应和深度神经网络的新颖解决方案，以应对应用机器学习进行危机相关数据分析的独特挑战，特别是大危机数据的数量和速度挑战。领域适应方法能够将信息从先前的源灾难转移到紧急的目标灾难。深度学习方法可以使用大量标记的源数据和未标记的目标数据，并随着更多标记的目标数据的可用而逐步更新模型。对源危机和目标危机组合进行大规模分析将有助于识别可转移态势感知知识的模式。由此产生的技术和社会解决方案将混合在一起，用于数据管理和应急响应。