CRII: CHS: Mining Intentions on Social Media to Enhance Situational Awareness of Crisis Response Organizations

CRII：CHS：挖掘社交媒体意图，增强危机应对组织的态势感知

• 批准号: 1657379
• 负责人: Hemant Purohit
• 金额: $ 17.5万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657379&HistoricalAwards=false
• 关键词: CRII; CHS; Mining; Intentions; Social

In large-scale emergencies, people post a lot of information about their status, needs, and abilities to help on social media. In principle, these posts might help emergency management teams get a better picture of the situation and find useful resources, but the number and questionable accuracy of these posts make them less useful than they could be. This project is about developing tools that identify people's intentions related to the emergency, sorting tweets into categories such as requests for help or information, offers of help, announcements of their safety or location, and so on. This problem of intent inference is a key scientific problem in natural language processing and artificial intelligence, with practical uses in a number of areas beyond emergency management, including web search and providing location-aware services. The researchers will attack the intent inference problem by narrowing it to the emergency response domain. First, they will work closely with emergency response teams to identify meaningful categories of intent that align with emergency response needs, in order to guide the collection and labeling of social media posts. Then, they will develop strategies drawn from existing image and natural language processing techniques and informed by the emergency response context to do the categorization work. Finally, they will build and evaluate a tool that uses the categorization algorithms to highlight the social media posts that are most likely to be useful to emergency responders. The work will be used to help develop courses around data science at the lead researcher's school, and the tools will be made publicly available through an open source code and advertised to communities of interest. To build the set of crisis-specific intent categories, the research team will first analyze existing operational manuals for emergency response including the Incident-Command-System models to extract key processes and initial categories, then refine that set working with experts from the Fairfax Fire and Rescue Department, an advisory committee of social media working group for emergency services at Department of Homeland Security that has members across the country, and members of the project's advisory board. Intent extraction will be modeled as a multilabel classification problem on two dimensions: type of intent, and topical category; this formulation maps well to characteristics of posts (which might contain multiple intents and topics) and scopes the complexity of general intent inference. Datasets will be gathered from prior crisis events and labeled by crowd workers interested in humanitarian work according to the categories identified from the first phase. Features of posts will be constructed from posts? metadata using natural language processing techniques on textual content, image processing techniques on multimedia content and author profiling techniques. Features will include extracting syntactic-semantic patterns that represent declarative and psycholinguistic knowledge as well as ideas from discourse analysis, while features of authors will be drawn from their provided profile information as well as aggregate inferences from their posts. The team will use a multi-task learning framework as the underlying algorithm to leverage relationships between the different categories to be classified. Finally, the developed interface will support faceted browsing by intent, topic, location, and response management process, and be evaluated through training exercises with the research team's practitioner partners.

在大规模紧急情况下，人们会在社交媒体上发布大量关于自己的状况、需求和帮助能力的信息。原则上，这些员额可能有助于应急管理团队更好地了解情况并找到有用的资源，但这些员额的数量和准确性令人怀疑，使它们没有应有的用处。这个项目是关于开发工具来识别人们与紧急情况相关的意图，将推文分类为请求帮助或信息、提供帮助、宣布他们的安全或位置等类别。意图推理问题是自然语言处理和人工智能中的一个关键科学问题，在应急管理以外的许多领域都有实际应用，包括网络搜索和提供位置感知服务。研究人员将通过将意图推理问题缩小到紧急响应领域来解决该问题。首先，他们将与应急团队密切合作，确定符合应急需求的有意义的意图类别，以指导社交媒体帖子的收集和标签。然后，他们将制定从现有图像和自然语言处理技术中提取的策略，并根据紧急响应上下文进行分类工作。最后，他们将构建和评估一个工具，该工具使用分类算法来突出显示最有可能对应急人员有用的社交媒体帖子。这项工作将用于在首席研究员所在的学校帮助开发围绕数据科学的课程，这些工具将通过开放源代码公开提供，并向感兴趣的社区发布广告。为了建立一套针对危机的意图类别，研究团队将首先分析现有的应急响应操作手册，包括事件指挥系统模型，以提取关键流程和初始类别，然后与来自费尔法克斯消防和救援部的专家、国土安全部紧急服务社交媒体工作组的咨询委员会和该项目顾问委员会的成员合作，完善这一套。意图提取将被建模为两个维度上的多标签分类问题：意图类型和主题类别；该公式很好地映射到帖子的特征(可能包含多个意图和主题)，并限定了一般意图推理的复杂性。数据集将从以前的危机事件中收集，并由对人道主义工作感兴趣的群众工作人员根据第一阶段确定的类别进行标记。柱子的特征将由柱子构造吗？在文本内容上使用自然语言处理技术的元数据，在多媒体内容上使用图像处理技术和作者简介技术。特征将包括提取句法-语义模式，代表陈述性和心理语言学知识以及来自语篇分析的想法，而作者的特征将从他们提供的个人资料信息中提取，以及从他们的帖子中汇总推论。该团队将使用多任务学习框架作为基本算法，以利用要分类的不同类别之间的关系。最后，开发的界面将支持按意图、主题、位置和响应管理流程进行分面浏览，并通过与研究团队的从业者合作伙伴进行培训练习进行评估。

项目成果

Classifying Relevant Social Media Posts During Disasters Using Ensemble of Domain-agnostic and Domain-specific Word Embeddings

使用与领域无关和特定领域的词嵌入集合对灾难期间的相关社交媒体帖子进行分类

• 发表时间: 2019
• 期刊: AAAI FSS-19: Artificial Intelligence for Social Good
• 作者: Nalluru, Ganesh;Pandey, Rahul;Purohit, Hemant
• 通讯作者: Purohit, Hemant

The Digital Crow's Nest: A Framework for Proactive Disaster Informatics & Resilience by Open Source Intelligence.

数字鸦巢：主动灾害信息学框架

• 发表时间: 2018
• 期刊: Proceedings of the ... International ISCRAM Conference
• 作者: Purohit, Hemant;Moore, Kathleen
• 通讯作者: Moore, Kathleen

CitizenHelper-Adaptive: Expert-Augmented Streaming Analytics System for Emergency Services and Humanitarian Organizations

CitizenHelper-Adaptive：适用于紧急服务和人道主义组织的专家增强流分析系统

• DOI: 10.1109/asonam.2018.8508374
• 发表时间: 2018
• 期刊: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM
• 作者: Pandey, Rahul;Purohit, Hemant
• 通讯作者: Purohit, Hemant

Ranking of Social Media Alerts with Workload Bounds in Emergency Operation Centers

• DOI: 10.1109/wi.2018.00-88
• 发表时间: 2018-09
• 期刊: 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)
• 作者: Hemant Purohit;C. Castillo;Muhammad Imran-;Rahul Pandey

Modeling Transportation Uncertainty in Matching Help Seekers and Suppliers during Disasters

对灾难期间匹配求助者和供应商的运输不确定性进行建模

• 发表时间: 2018
• 期刊: First Workshop on Intelligent Transportation Informatics at the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR
• 作者: Purohit, Hemant;Vedula, Nikhita;Thirunarayan, Krishnaprasad;Parthasarathy, Srinivasan
• 通讯作者: Parthasarathy, Srinivasan