  Abusive content in online social networks is a well-known problem that can
cause serious psychological harm and incite hatred. The ability to upload audio
data increases the importance of developing methods to detect abusive content
in speech recordings. However, simply transferring the mechanisms from written
abuse detection would ignore relevant information such as emotion and tone. In
addition, many current algorithms require training in the specific language for
which they are being used. This paper proposes to use acoustic and prosodic
features to classify abusive content. We used the ADIMA data set, which
contains recordings from ten Indic languages, and trained different models in
multilingual and cross-lingual settings. Our results show that it is possible
to classify abusive and non-abusive content using only acoustic and prosodic
features. The most important and influential features are discussed.
