Current online moderation follows a one-size-fits-all approach, where each
intervention is applied in the same way to all users. This naive approach is
challenged by established socio-behavioral theories and by recent empirical
results that showed the limited effectiveness of such interventions. We propose
a paradigm-shift in online moderation by moving towards a personalized and
user-centered approach. Our multidisciplinary vision combines state-of-the-art
theories and practices in diverse fields such as computer science, sociology
and psychology, to design personalized moderation interventions (PMIs). In
outlining the path leading to the next-generation of moderation interventions,
we also discuss the most prominent challenges introduced by such a disruptive
change.

该研究提出一种基于个体用户的在线管理方法，该方法融合了计算机科学、社会学和心理学等领域的前沿理论和实践，并探讨了此类革新性变革所带来的最重要挑战。

在线管理的个性化干预

Personalized Interventions for Online Moderation

The act of appearing kind or helpful via the use of but having a feeling of
superiority condescending and patronizing language can have have serious mental
health implications to those that experience it. Thus, detecting this
condescending and patronizing language online can be useful for online
moderation systems. Thus, in this manuscript, we describe the system developed
by Team UTSA SemEval-2022 Task 4, Detecting Patronizing and Condescending
Language. Our approach explores the use of several deep learning architectures
including RoBERTa, convolutions neural networks, and Bidirectional Long
Short-Term Memory Networks. Furthermore, we explore simple and effective
methods to create ensembles of neural network models. Overall, we experimented
with several ensemble models and found that the a simple combination of five
RoBERTa models achieved an F-score of .6441 on the development dataset and
.5745 on the final test dataset. Finally, we also performed a comprehensive
error analysis to better understand the limitations of the model and provide
ideas for further research.

检测网络中的居高临下和傲慢语言对遭受其影响的人的心理健康具有严重影响，因此作者通过多种深度学习模型和神经网络模型的组合，成功开发了一种在线审核系统，可实时检测这种语言，并进行有效的处理。研究结果显示，该模型的 F 值在开发数据集和最终测试数据集上分别为 0.6441 和 0.5745。同时，作者还进行了全面的误差分析，以进一步深入研究其限制性和扩展性。