  Not all topics are equally "flammable" in terms of toxicity: a calm
discussion of turtles or fishing less often fuels inappropriate toxic dialogues
than a discussion of politics or sexual minorities. We define a set of
sensitive topics that can yield inappropriate and toxic messages and describe
the methodology of collecting and labeling a dataset for appropriateness. While
toxicity in user-generated data is well-studied, we aim at defining a more
fine-grained notion of inappropriateness. The core of inappropriateness is that
it can harm the reputation of a speaker. This is different from toxicity in two
respects: (i) inappropriateness is topic-related, and (ii) inappropriate
message is not toxic but still unacceptable. We collect and release two
datasets for Russian: a topic-labeled dataset and an appropriateness-labeled
dataset. We also release pre-trained classification models trained on this
data.
