Abstract:
In this paper, we present a dataset that contains 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 well laid out annotator guidelines and data extraction procedure. 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 in general to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment. The dataset can be found at \url{ https://doi.org/10.7910/DVN/JN4EYU}.
