Abstract: Computer-implemented techniques for accurate and interpretable toxic content detection are disclosed. The techniques encompass using a probabilistic toxic keyword identifier to probabilistically determine keywords that are indicative of toxic content. In an implementation, the toxic keywords are determined based on comparing term frequencies of the keywords in a set of example toxic interpersonal electronic communications against term frequencies of the keywords in a set of example non-toxic interpersonal electronic communications. A keyword is determined as indicative of toxic content if its term frequency in the set of toxic examples is more than a threshold number of times more than its term frequency in the set of non-toxic examples. In this way, a set of multiple keywords indicative of toxic content can be determined. Survey comments containing a keyword determined to be toxic are then flagged as potential toxic content in a user interface for human review.