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Applying Machine Learning to Big Data using Social Media analysis to identify people with high intelligence

Burke, Shane (2015) Applying Machine Learning to Big Data using Social Media analysis to identify people with high intelligence. Masters thesis, Dublin, National College of Ireland.

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Abstract

Since the rise of social media platforms such as Facebook and Twitter, companies and organisations have performed social media analysis or data mining to help better understand their existing customers and to seek out potential new ones. This research has set about using this technique coupled with machine learning algorithms to explore the question of being able to identify highly intelligent people solely on their social media data. Given that there are millions of people worldwide sharing and collaborating online, the abundance of available data is potentially unlimited. The data collected as part of this research will be stored in a Big Data framework to ensure this work will be able to cope with the vast amounts of data available.

It was concluded that it’s not possible to distinguish highly intelligent people by solely analysing their social media data. The observations from analysing over 1 million tweets show that social media users regularly boycott the use correct grammar and punctuation. The findings do suggest that Twitter’s character restriction has a large influence on the quality of content posted.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks
T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions: School of Computing > Msc.: Master of Science in Web Technologies
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 20 Oct 2015 09:23
Last Modified: 20 Oct 2015 09:23
URI: http://trap.ncirl.ie/id/eprint/2103

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