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Twitrends: A Real Time Trending Topics Detection System for Twitter Social Network

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Cosmina Ivan, Andrei Moldovan
10.5120/ijca2016911785

Cosmina Ivan and Andrei Moldovan. Twitrends: A Real Time Trending Topics Detection System for Twitter Social Network. International Journal of Computer Applications 152(4):16-25, October 2016. BibTeX

@article{10.5120/ijca2016911785,
	author = {Cosmina Ivan and Andrei Moldovan},
	title = {Twitrends: A Real Time Trending Topics Detection System for Twitter Social Network},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2016},
	volume = {152},
	number = {4},
	month = {Oct},
	year = {2016},
	issn = {0975-8887},
	pages = {16-25},
	numpages = {10},
	url = {http://www.ijcaonline.org/archives/volume152/number4/26307-2016911785},
	doi = {10.5120/ijca2016911785},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Big Data processing applications have become popular in the last few years. One of the main reasons is that the data generated by current systems and applications is more complex, have a higher speed and its volume increases exponentially. Another reason would be that the traditional methods for data processing and storage are obsolete and the new tools and frameworks brought a lot of advantages. Various social networks need to process big volumes of data, and users take into consideration the speed and quality of the process. We propose an initial approach for processing data from Twitter social network, in a system which allows a real-time classification of tweets based on topics and user location. With this approach we argue that in a dynamic world, were data increases exponentially and the processing needs to be very fast, the proposed system is capable to determine trending topics in real time.

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Keywords

Twitter, trending topics, real-time, geolocation, Big Data