HopeEDI: A Multilingual Hope Speech Detection Dataset for Equality, Diversity, and Inclusion

Bharathi Raja Chakravarthi


Abstract
Over the past few years, systems have been developed to control online content and eliminate abusive, offensive or hate speech content. However, people in power sometimes misuse this form of censorship to obstruct the democratic right of freedom of speech. Therefore, it is imperative that research should take a positive reinforcement approach towards online content that is encouraging, positive and supportive contents. Until now, most studies have focused on solving this problem of negativity in the English language, though the problem is much more than just harmful content. Furthermore, it is multilingual as well. Thus, we have constructed a Hope Speech dataset for Equality, Diversity and Inclusion (HopeEDI) containing user-generated comments from the social media platform YouTube with 28,451, 20,198 and 10,705 comments in English, Tamil and Malayalam, respectively, manually labelled as containing hope speech or not. To our knowledge, this is the first research of its kind to annotate hope speech for equality, diversity and inclusion in a multilingual setting. We determined that the inter-annotator agreement of our dataset using Krippendorff’s alpha. Further, we created several baselines to benchmark the resulting dataset and the results have been expressed using precision, recall and F1-score. The dataset is publicly available for the research community. We hope that this resource will spur further research on encouraging inclusive and responsive speech that reinforces positiveness.
Anthology ID:
2020.peoples-1.5
Volume:
Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venues:
COLING | PEOPLES
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–53
Language:
URL:
https://www.aclweb.org/anthology/2020.peoples-1.5
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.peoples-1.5.pdf