Event detection on large social media using temporal analysis

Social media networks are now considered as one of the major news channels that breaks news as they fold. The problem of event detection based on social media has attracted researchers' attention recently because of the enormous popularity of social media. Existing approaches focus on features...

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Bibliographic Details
Published in2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC) pp. 1 - 6
Main Authors Aldhaheri, Abdulrahman, Jeongkyu Lee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2017
Subjects
Big data
Clustering algorithms
data mining
Event detection
Feature extraction
Machine learning
Media
Shape
Social media analysis
Twitter
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Summary:Social media networks are now considered as one of the major news channels that breaks news as they fold. The problem of event detection based on social media has attracted researchers' attention recently because of the enormous popularity of social media. Existing approaches focus on features that don't reflect full characteristics of the social network. For the purpose of this research, we define an event as an occurrence that has enough force and momentum that could create an observable change of the context of a social network. Such a definition provides us with a wider perspective through which we can view the big picture of the social network. In this research, we propose a novel framework for detecting events on social media. We introduce a temporal approach to detect structural change of the social network that reflects an occurrence of an event using machine learning algorithms. In this study, we show that processing temporal social networks captures the complete complexity of the social network, which results in a higher accuracy of event detection.
DOI:10.1109/CCWC.2017.7868467