Statistical Analysis and Classification of Calamity Related Tweets

Abstract

Twitter and other social media platforms
have emerged as popular channels for
communication during emergencies. Social
networks generate huge amounts of data due
to the behaviour of their users. A wide range
of topics is discussed on social networks,
including politics, health issues, and natural
disasters. Therefore, public data provides a
wealth of information on many topics.
Traditional methods of communication have
been enhanced in many ways by the Internet.
In disaster assessment, Machine Learning
(ML) and Artificial Intelligence (AI)
algorithms are becoming increasingly
popular. The use of micro blogging platforms
such as Twitter during natural catastrophes
and emergencies generates an increasing
number of posts on these platforms. In this
paper, we examine natural disasters,
including avalanches, tornadoes, hurricanes,
droughts, earthquakes, landslides, tsunamis,
floods, volcanoes, and wildfires. We extract
data from Twitter Network and classify them
as disaster and non-disaster tweets as target
and non-targets using SVM, Word2Vec, TFIDF, and BERT model.