  In this paper, we present a dataset containing 9,973 tweets related to the
MeToo movement that were manually annotated for five different linguistic
aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present
a detailed account of the data collection and annotation processes. The
annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha)
due to the domain expertise of the annotators and clear annotation
instructions. We analyze the data in terms of geographical distribution, label
correlations, and keywords. Lastly, we present some potential use cases of this
dataset. We expect this dataset would be of great interest to psycholinguists,
socio-linguists, and computational linguists to study the discursive space of
digitally mobilized social movements on sensitive issues like sexual
harassment.
