  We present a new dataset of approximately 44000 comments labeled by
crowdworkers. Each comment is labelled as either 'healthy' or 'unhealthy', in
addition to binary labels for the presence of six potentially 'unhealthy'
sub-attributes: (1) hostile; (2) antagonistic, insulting, provocative or
trolling; (3) dismissive; (4) condescending or patronising; (5) sarcastic;
and/or (6) an unfair generalisation. Each label also has an associated
confidence score. We argue that there is a need for datasets which enable
research based on a broad notion of 'unhealthy online conversation'. We build
this typology to encompass a substantial proportion of the individual comments
which contribute to unhealthy online conversation. For some of these
attributes, this is the first publicly available dataset of this scale. We
explore the quality of the dataset, present some summary statistics and initial
models to illustrate the utility of this data, and highlight limitations and
directions for further research.
