The growth of social networks makes toxic content spread rapidly. Hate speech
detection is a task to help decrease the number of harmful comments. With the
diversity in the hate speech created by users, it is necessary to interpret the
hate speech besides detecting it. Hence, we propose a methodology to construct
a system for targeted hate speech detection from online streaming texts from
social media. We first introduce the ViTHSD - a targeted hate speech detection
dataset for Vietnamese Social Media Texts. The dataset contains 10K comments,
each comment is labeled to specific targets with three levels: clean,
offensive, and hate. There are 5 targets in the dataset, and each target is
labeled with the corresponding level manually by humans with strict annotation
guidelines. The inter-annotator agreement obtained from the dataset is 0.45 by
Cohen's Kappa index, which is indicated as a moderate level. Then, we construct
a baseline for this task by combining the Bi-GRU-LSTM-CNN with the pre-trained
language model to leverage the power of text representation of BERTology.
Finally, we suggest a methodology to integrate the baseline model for targeted
hate speech detection into the online streaming system for practical
application in preventing hateful and offensive content on social media.

社交媒体上激进内容的快速传播促使了对仇恨言论的检测任务的需求，本论文提供了一种构建用于针对性仇恨言论检测的系统的方法，并介绍了一个越南社交媒体文本的针对性仇恨言论检测数据集，并结合预训练语言模型与基于文本表征的 Bi-GRU-LSTM-CNN 模型建立了基线模型，最终提出了一种方法将该模型整合至在线流媒体系统，以在社交媒体上防止令人讨厌和冒犯性内容的实际应用。

利用受害者的仇恨进行越南社交媒体文本中的仇恨言论检测

Exploiting Hatred by Targets for Hate Speech Detection on Vietnamese  Social Media Texts

Online streaming is an emerging market that address much attention. Assessing
gaming skills from videos is an important task for streaming service providers
to discover talented gamers. Service providers require the information to offer
customized recommendation and service promotion to their customers. Meanwhile,
this is also an important multi-modal machine learning tasks since online
streaming combines vision, audio and text modalities. In this study we begin by
identifying flaws in the dataset and proceed to clean it manually. Then we
propose several variants of latest end-to-end models to learn joint
representation of multiple modalities. Through our extensive experimentation,
we demonstrate the efficacy of our proposals. Moreover, we identify that our
proposed models is prone to identifying users instead of learning meaningful
representations. We purpose future work to address the issue in the end.

在线流媒体对游戏技能进行评估是一个重要任务，以帮助发现有才华的玩家，该研究通过清理数据集和提出多模态机器学习模型的变体来解决这一问题，同时发现了模型容易识别用户而不是学习有意义的表示的问题。