An event detection technique using social media data
Muskan Garg

TL;DR
This paper presents a new method for detecting events from social media data, especially Twitter, by extracting keyphrases and analyzing their co-occurrence to improve event identification accuracy.
Contribution
It introduces a three-phase approach that enhances lexical variation handling and sub-event detection in social media streams.
Findings
Improved event detection accuracy on Twitter data
Effective extraction of keyphrases from noisy social media text
Enhanced identification of sub-events and ranking of event phrases
Abstract
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…
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Complex Network Analysis Techniques · Web Data Mining and Analysis
