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Akpofure, Tuvie (2019) Automatic Identification of Hate Speech on Social Media Platforms using Machine Learning. Masters thesis, Dublin, National College of Ireland.
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Abstract
In recent years, the main medium for communication and dissemination of information amongst internet users has been social media platforms such as Facebook, Twitter, etc. These platforms have experienced massive growth and have gained popularity globally based on the features, functions, exposure and benefits attached to them. Furthermore, the increase in popularity of social media platforms has also led to the increase and spread of cybercrimes on these platforms. Some of the cybercrimes that have gained prominence on these platforms are as follow; spamming, phishing, social engineering, etc.
In this research work, our aim is to automatically detect hate speech (which is a type of spam) from tweets sent by users on Twitter. Hate speech could be said to be messages that are offensive, intimidating or insulting targeted at or a group based on their religion, race, ethnicity, sex, etc. Hate speech sometimes leads to physical hate crimes which could be very devastating. Therefore, we used a labelled open source hate speech dataset to help generate features to train our Naïve Bayes model. The model had a precision score of 83% in classifying hate from non-hate comments.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software H Social Sciences > HV Social pathology. Social and public welfare > Criminology > Crimes and Offences > Cyber Crime |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Caoimhe Ní Mhaicín |
| Date Deposited: | 03 Apr 2020 13:14 |
| Last Modified: | 03 Apr 2020 13:14 |
| URI: | https://norma.ncirl.ie/id/eprint/4172 |
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