  People post information about different topics which are in their active
vocabulary over social media platforms (like Twitter, Facebook, PInterest and
Google+). They follow each other and it is more likely that the person who
posts information about current happenings will receive better response. Manual
analysis of huge amount of data on social media platforms is difficult. This
has opened new research directions for automatic analysis of usercontributed
social media documents. Automatic social media data analysis is difficult due
to abundant information shared by users. Many researchers use Twitter data for
Social Media Analysis (SMA) as the Twitter data is freely available in the
public domain. One of the most this research work. Event Detection from social
media data is used for different applications like traffic congestion
detection, disaster and emergency management, and live news detection. Nature
of the information which is shared on twitter platform is short-text, noisy,
and ambiguous. Thus, event detection and extraction of event phrases from
user-generated and illformed data becomes challenging. To address these
challenges, events are extracted from streaming social media data in the form
of keyphrases using different cognitive properties. The motivation behind this
research work is to provide substantial improvements in the lexical variation
of event phrases while detecting events and sub-events from twitter data. In
this research work, the approach towards event detection from social media data
is divided into three phases namely: Identifying sub-graphs in Microblog Word
Co-occurrence Network (WCN) which provides important information about
keyphrases; Identifying multiple events from social media data; and Ranking
contextual information of event phrases.
