5 research outputs found
Tackling Hate Speech in Low-resource Languages with Context Experts
Given Myanmars historical and socio-political context, hate speech spread on
social media has escalated into offline unrest and violence. This paper
presents findings from our remote study on the automatic detection of hate
speech online in Myanmar. We argue that effectively addressing this problem
will require community-based approaches that combine the knowledge of context
experts with machine learning tools that can analyze the vast amount of data
produced. To this end, we develop a systematic process to facilitate this
collaboration covering key aspects of data collection, annotation, and model
validation strategies. We highlight challenges in this area stemming from small
and imbalanced datasets, the need to balance non-glamorous data work and
stakeholder priorities, and closed data-sharing practices. Stemming from these
findings, we discuss avenues for further work in developing and deploying hate
speech detection systems for low-resource languages.Comment: ICTD 2022 Conference pape