Automatic Cyberbullying Detection: A Semi-supervised Machine Learning Approach
| Autor: | Ting-Yu HUANG, 黃亭瑜 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Social media is an indispensable online web platform today. With such a platform, people can more freely and quickly spread many creations or content, including Facebook, Twitter, Instagram, LINE, Youtube, Dcard, etc. Although such a platform can communicate, connect, and interact instantly, there are also aggressive behaviors, and cyberbullying is one of them. Because social media can be instantly spread without geographical restrictions, and the characteristics of anonymity,Therefore, many users do not need to indicate their true identity, and can quickly and massively disseminate content, including threatening intimidation, verbal abuse, and other bullying behaviors, so they must actively fight against Internet bullying. Our research uses the social media Formspring Q&A article as a text material, using the swear words provided by NoSwearing as a bad word dictionary, and the text, statistics, readability and emotional features most used in past studies as features of our research. However, because supervised learning requires manual data sets as training materials, it will consume a lot of manpower and time costs. Therefore, it is hoped that through the semi-supervised learning technology, the bullying article detection classifier can be learned. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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