期刊
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
卷 118, 期 -, 页码 219-229出版社
ELSEVIER
DOI: 10.1016/j.future.2021.01.006
关键词
Cyberbullying; Social networks; Early detection; Machine learning
资金
- Ministry of Economy and Competitiveness of Spain
- FEDER funds of the European Union [PID2019-111388GB-I00]
- Centro de Investigacion de Galicia CITIC - Xunta de Galicia (Galicia, Spain)
- European Union (European Regional Development Fund - Galicia 2014-2020 Program) [ED431G 2019/01]
The article discusses different approaches to early detection of cyberbullying on social networks, proposing two sets of features and two early detection methods that have successfully improved baseline detection models by up to 42%.
Cyberbullying is an important issue for our society and has a major negative effect on the victims, that can be highly damaging due to the frequency and high propagation provided by Information Technologies. Therefore, the early detection of cyberbullying in social networks becomes crucial to mitigate the impact on the victims. In this article, we aim to explore different approaches that take into account the time in the detection of cyberbullying in social networks. We follow a supervised learning method with two different specific early detection models, named threshold and dual. The former follows a more simple approach, while the latter requires two machine learning models. To the best of our knowledge, this is the first attempt to investigate the early detection of cyberbullying. We propose two groups of features and two early detection methods, specifically designed for this problem. We conduct an extensive evaluation using a real world dataset, following a time-aware evaluation that penalizes late detections. Our results show how we can improve baseline detection models up to 42%. (C) 2021 The Authors. Published by Elsevier B.V.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据