  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, and shelter, using
logistic regression shows promising results and outperforms existing works.
Finally, we introduce an architecture to store intermediate data to accelerate
the development process of the machine learning classifiers.
