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

在大规模的紧急情况下，人们会在社交媒体上发布大量关于他们的状态、需求和帮助能力的信息。原则上，这些帖子可能有助于应急管理小组更好地了解情况并找到有用的资源，但这些帖子的数量和令人怀疑的准确性使它们的作用大大降低。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，如请求帮助或信息，提供帮助，宣布他们的安全或位置，等等。这种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，在应急管理之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。研究人员将通过将其缩小到应急响应领域来解决意图推理问题。首先，他们将与应急小组密切合作，确定符合应急需求的有意义的意图类别，以指导收集和标记社交媒体帖子。然后，他们将从现有的图像和自然语言处理技术中制定策略，并根据应急响应背景进行分类工作。最后，他们将建立并评估一个工具，该工具使用分类算法来突出显示最有可能对紧急救援人员有用的社交媒体帖子。这项工作将用于帮助开发首席研究员所在学校的数据科学课程，这些工具将通过开源代码公开提供，并向感兴趣的社区宣传。为了建立一套危机特定意图类别，研究小组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，然后与费尔法克斯消防和救援部门的专家合作，完善该系列。费尔法克斯消防和救援部门是国土安全部紧急服务社交媒体工作组的咨询委员会，其成员遍布全国。以及项目顾问委员会的成员。意图提取将在两个维度上建模为一个多标签分类问题：意图类型和主题类别；这个公式很好地映射到帖子的特征（可能包含多个意图和主题），并限制了一般意图推理的复杂性。数据集将从先前的危机事件中收集，并由对人道主义工作感兴趣的人群工作人员根据第一阶段确定的类别进行标记。帖子的功能将由帖子构建吗？元数据在文本内容上使用自然语言处理技术，在多媒体内容上使用图像处理技术和作者剖析技术。功能将包括提取语法语义模式，这些模式代表陈述性和心理语言学知识，以及话语分析中的想法，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断。该团队将使用多任务学习框架作为底层算法，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的培训练习进行评估。

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.