A Human-Centered Systematic Literature Review of Cyberbullying Detection Algorithms
A Human-Centered Systematic Literature Review of Cyberbullying Detection Algorithms
复制标题
以人为中心的网络欺凌检测算法的系统文献综述
DOI：
10.1145/3476066
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发表时间：
2021
影响因子：
--
通讯作者：
De Choudhury, Munmun
中科院分区：
文献类型：
--
作者：
Kim, Seunghyun;Razi, Afsaneh;Stringhini, Gianluca;Wisniewski, Pamela J.;De Choudhury, Munmun
网络欺凌是社交媒体平台上一个日益严重的问题，给受害者造成短期和长期的影响。为了缓解这个问题，研究人员着眼于构建由机器学习驱动的自动化系统，以检测网络欺凌事件或受害者和肇事者等相关行为者。过去，系统评价研究了这一不断增长的工作中的方法，但重点关注技术创新、特征工程或性能优化的计算方面，而不以人类的角色、信仰、愿望或期望为中心。在本文中，我们对过去 10 年的自动网络欺凌检测研究进行了以人为本的系统文献综述。我们基于三管齐下的以人为本的算法设计框架（涵盖理论设计、参与式设计和推测性设计）分析了 56 篇论文。我们发现，过去的文献在多个方面都未能融入以人为本，从定义网络欺凌、建立数据注释中的基本事实、评估检测模型的性能，到推测模型的使用和用户，包括潜在的危害和负面后果。考虑到网络欺凌经历的敏感性以及网络欺凌事件对相关行为者造成的深远影响，我们讨论了如何将以人为本纳入未来的研究中，以帮助开发更实用、更有用的检测系统，并适应利益相关者的不同需求和背景。
Cyberbullying is a growing problem across social media platforms, inflicting short and long-lasting effects on victims. To mitigate this problem, research has looked into building automated systems, powered by machine learning, to detect cyberbullying incidents, or the involved actors like victims and perpetrators. In the past, systematic reviews have examined the approaches within this growing body of work, but with a focus on the computational aspects of the technical innovation, feature engineering, or performance optimization, without centering around the roles, beliefs, desires, or expectations of humans. In this paper, we present a human-centered systematic literature review of the past 10 years of research on automated cyberbullying detection. We analyzed 56 papers based on a three-prong human-centeredness algorithm design framework - spanning theoretical, participatory, and speculative design. We found that the past literature fell short of incorporating human-centeredness across multiple aspects, ranging from defining cyberbullying, establishing the ground truth in data annotation, evaluating the performance of the detection models, to speculating the usage and users of the models, including potential harms and negative consequences. Given the sensitivities of the cyberbullying experience and the deep ramifications cyberbullying incidents bear on the involved actors, we discuss takeaways on how incorporating human-centeredness in future research can aid with developing detection systems that are more practical, useful, and tuned to the diverse needs and contexts of the stakeholders.
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DOI：
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发表时间：
2012
期刊：
影响因子：
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作者：
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DOI：
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发表时间：
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影响因子：
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