How We Define Harm Impacts Data Annotations: Explaining How Annotators
  Distinguish Hateful, Offensive, and Toxic Comments
Angela Schöpke-Gonzalez Siqi Wu Sagar Kumar Paul J. Resnick Libby Hemphill
Computational social science research has made advances in machine learning
and natural language processing that support content moderators in detecting
harmful content. These advances often rely on training datasets annotated by
crowdworkers for harmful content. In designing instructions for annotation
tasks to generate training data for these algorithms, researchers often treat
the harm concepts that we train algorithms to detect - 'hateful', 'offensive',
'toxic', 'racist', 'sexist', etc. - as interchangeable. In this work, we
studied whether the way that researchers define 'harm' affects annotation
outcomes. Using Venn diagrams, information gain comparisons, and content
analyses, we reveal that annotators do not use the concepts 'hateful',
'offensive', and 'toxic' interchangeably. We identify that features of harm
definitions and annotators' individual characteristics explain much of how
annotators use these terms differently. Our results offer empirical evidence
discouraging the common practice of using harm concepts interchangeably in
content moderation research. Instead, researchers should make specific choices
about which harm concepts to analyze based on their research goals. Recognizing
that researchers are often resource constrained, we also encourage researchers
to provide information to bound their findings when their concepts of interest
differ from concepts that off-the-shelf harmful content detection algorithms
identify. Finally, we encourage algorithm providers to ensure their instruments
can adapt to contextually-specific content detection goals (e.g., soliciting
instrument users' feedback).