Abstract:
Catastrophic events create uncertain environments in which it becomes very difficult to locate
affected people and provide them aids. People turn to Twitter during disasters for requesting
help/providing relief to others than their friends and family. Some applications exist to facilitate
people in such events, however; they follow very rigid formats and templates for posting tweets.
Both affected citizens and volunteers mostly do not strictly follow these formats for seeking
and providing help. A huge number of posts issued online for seeking help could not properly
be detected and remained concealed. A proportion of these posts, such as a request for seeking
help and relief, need instant attention. Affected citizens make requests for different types of
things, e.g., medical, volunteer, cloth, food, shelter, money. This research aims to delve into
tweets for extracting different types of request tweets (so-called rweets) in order to provide
humanitarian relief to both seekers and responders. In this work, the problem of automatic rweet
identification and categorization on Twitter is addressed. For rweet identification, both rulebased
and machine learning approaches are utilized while for rweet categorization, only
machine learning approach is employed. An empirical study comprising of a large number of
tests is performed to assess the problem from different aspects. For rweet identification,
precision of 99.72% achieved using rule-based approach and F-measure of 82.35% using
logistic regression in machine learning approach. Logistic regression also outperformed in
rweet categorization by gaining excellent F-measure of 94.95%. This research also provides
the practical guidance to machine learning practitioners and professionals while solving and
assessing classification problems considering different types of aspects. This study is limited
to the use of tweets contents as features. Although it is very powerful approach for extracting
information, but highly dependent upon manually labelled dataset for a specific disaster.
