  The widespread dissemination of toxic online posts is increasingly damaging
to society. However, research on detecting toxic language in Chinese has lagged
significantly. Existing datasets lack fine-grained annotation of toxic types
and expressions, and ignore the samples with indirect toxicity. In addition, it
is crucial to introduce lexical knowledge to detect the toxicity of posts,
which has been a challenge for researchers. In this paper, we facilitate the
fine-grained detection of Chinese toxic language. First, we built Monitor Toxic
Frame, a hierarchical taxonomy to analyze toxic types and expressions. Then, a
fine-grained dataset ToxiCN is presented, including both direct and indirect
toxic samples. We also build an insult lexicon containing implicit profanity
and propose Toxic Knowledge Enhancement (TKE) as a benchmark, incorporating the
lexical feature to detect toxic language. In the experimental stage, we
demonstrate the effectiveness of TKE. After that, a systematic quantitative and
qualitative analysis of the findings is given.
