  The problem of online threats and abuse could potentially be mitigated with a
computational approach, where sources of abuse are better understood or
identified through author profiling. However, abusive language constitutes a
specific domain of language for which it has not yet been tested whether
differences emerge based on a text author's personality, age, or gender. This
study examines statistical relationships between author demographics and
abusive vs normal language, and performs prediction experiments for
personality, age, and gender. Although some statistical relationships were
established between author characteristics and language use, these patterns did
not translate to high prediction performance. Personality traits were predicted
within 15% of their actual value, age was predicted with an error margin of 10
years, and gender was classified correctly in 70% of the cases. These results
are poor when compared to previous research on author profiling, therefore we
urge caution in applying this within the context of abusive language and threat
assessment.
