In recent years, the rising use of social media has propelled automated
cyberbullying detection into a prominent research domain. However, challenges
persist due to the absence of a standardized definition and universally
accepted datasets. Many researchers now view cyberbullying as a facet of
cyberaggression, encompassing factors like repetition, peer relationships, and
harmful intent in addition to online aggression. Acquiring comprehensive data
reflective of all cyberbullying components from social media networks proves to
be a complex task. This paper provides a description of an extensive
semi-synthetic cyberbullying dataset that incorporates all of the essential
aspects of cyberbullying, including aggression, repetition, peer relationships,
and intent to harm. The method of creating the dataset is succinctly outlined,
and a detailed overview of the publicly accessible dataset is additionally
presented. This accompanying data article provides an in-depth look at the
dataset, increasing transparency and enabling replication. It also aids in a
deeper understanding of the data, supporting broader research use.

本研究提供了一个广泛的半合成网络欺凌数据集的描述，包括网络欺凌的所有关键要素。该数据集的创建方法简洁地概述，并且还提供了详细的公开可访问数据集的概述。这篇伴随数据文章深入探讨了数据集，增加了透明度并支持复制研究。它还有助于更深入地理解数据，支持更广泛的研究使用。

一个多方面的半合成数据集用于自动化网络欺凌检测

A Multi-faceted Semi-Synthetic Dataset for Automated Cyberbullying  Detection

Cyberbullying and cyberaggression are increasingly worrisome phenomena
affecting people across all demographics. More than half of young social media
users worldwide have been exposed to such prolonged and/or coordinated digital
harassment. Victims can experience a wide range of emotions, with negative
consequences such as embarrassment, depression, isolation from other community
members, which embed the risk to lead to even more critical consequences, such
as suicide attempts.
In this work, we take the first concrete steps to understand the
characteristics of abusive behavior in Twitter, one of today's largest social
media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing
users participating in discussions around seemingly normal topics like the NBA,
to those more likely to be hate-related, such as the Gamergate controversy, or
the gender pay inequality at the BBC station. We also explore specific
manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in
one of the hate-related communities (Gamergate). We present a robust
methodology to distinguish bullies and aggressors from normal Twitter users by
considering text, user, and network-based attributes. Using various
state-of-the-art machine learning algorithms, we classify these accounts with
over 90% accuracy and AUC. Finally, we discuss the current status of Twitter
user accounts marked as abusive by our methodology, and study the performance
of potential mechanisms that can be used by Twitter to suspend users in the
future.

本研究使用机器学习算法对 Twitter 上的滥用行为进行分类，包括网络欺凌和侵略行为，并通过分析用户、网络和文本属性的差异识别出欺负和侵略者。结果表明，该算法能够以超过 90％的准确率和 AUC 分类这些账户。同时，探讨了 Twitter 标记为滥用行为的用户账户的状况，并研究了 Twitter 可能采用的用户封禁机制的效果。