Papers › Implicitly Abusive Comparisons -- A New Dataset and Linguistic Analysis
Implicitly Abusive Comparisons -- A New Dataset and Linguistic Analysis
Michael Wiegand, Maja Geulig, Josef Ruppenhofer
We examine the task of detecting implicitly abusive comparisons (e.g. {``}Your hair looks like you have been electrocuted{''}). Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g. {``}dumbass{''} or {``}scum{''}) are absent. We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons. We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons.
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