  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.
