Catastrophic events create uncertain situations for humanitarian organizations locating and providing aid to affected people. Many people turn to social media during disasters for requesting help and/or providing relief to others. However, the majority of social media posts seeking help could not properly be detected and remained concealed because often they are noisy and ill-formed. Existing systems lack in planning an effective strategy for tweet preprocessing and grasping the contexts of tweets. This research, first of all, formally defines request tweets in the context of social networking sites, hereafter rweets, along with their different primary types and sub-types. Our main contributions are the identification and categorization of rweets. For rweet identification, we employ two approaches, namely a rule-based and logistic regression, and show their high precision and F1 scores. The rweets classification into sub-types such as medical, food, shelter, using logistic regression shows promising results and outperforms exiting works. Finally, we introduce an architecture to store intermediate data to accelerate the development process of the machine learning classifiers.

灾难性事件为人道主义组织寻找受影响的人并提供援助提供了不确定的条件。在灾难期间，许多人向社交媒体寻求帮助和/或向他人提供救济。但是，大多数寻求帮助的社交媒体帖子都无法正确检测到并被隐藏起来，因为它们经常嘈杂且格式不正确。现有系统缺乏计划用于推文预处理和掌握推文上下文的有效策略。首先，这项研究在社交网站的上下文中正式定义了请求推文，此后称为推文，以及它们的不同主要类型和子类型。我们的主要贡献是对推文的识别和分类。对于rweet识别，我们采用两种方法，即基于规则的回归和逻辑回归，并展示他们的高精度和F1分数。使用logistic回归将rweets分为医疗，食品，住所等子类型，显示出令人鼓舞的结果，胜过现有作品。最后，我们介绍了一种存储中间数据的体系结构，以加快机器学习分类器的开发过程。