From Trolling to Cyberbullying: Using Machine Learning and Network Analysis to Study Anti-Social Behavior on Social Media

被引：0

作者：

Gruzd, Anatoliy ^{[1
]}

Mai, Philip ^{[1
]}

Soares, Felipe Bonow ^{[2
]}

机构：

[1] Toronto Metropolitan Univ, Social Media Lab, Toronto, ON, Canada

[2] Univ Arts London, London Coll Commun, London, England

来源：

34TH ACM CONFERENCE ON HYPERTEXT AND SOCIAL MEDIA, HT 2023 | 2023年

关键词：

anti-social; online discourse; toxicity analysis; social network analysis; trolling; cyberbullying; computational social science;

D O I：

10.1145/3603163.3610531

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The rise of social media and other web and mobile applications has transformed how people interact, but it has also created new challenges, such as anti-social behavior like trolling, cyberbullying, and hate speech. This behavior can have severe negative consequences for individuals and communities. This tutorial is intended for researchers and practitioners interested in computational social science and provides an overview of how to use machine learning and social network analysis techniques to detect and examine anti-social behavior in online discourse.

引用

页数：2

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