  The widespread of offensive content online, such as hate speech and
cyber-bullying, is a global phenomenon. This has sparked interest in the
artificial intelligence (AI) and natural language processing (NLP) communities,
motivating the development of various systems trained to detect potentially
harmful content automatically. These systems require annotated datasets to
train the machine learning (ML) models. However, with a few notable exceptions,
most datasets on this topic have dealt with English and a few other
high-resource languages. As a result, the research in offensive language
identification has been limited to these languages. This paper addresses this
gap by tackling offensive language identification in Sinhala, a low-resource
Indo-Aryan language spoken by over 17 million people in Sri Lanka. We introduce
the Sinhala Offensive Language Dataset (SOLD) and present multiple experiments
on this dataset. SOLD is a manually annotated dataset containing 10,000 posts
from Twitter annotated as offensive and not offensive at both sentence-level
and token-level, improving the explainability of the ML models. SOLD is the
first large publicly available offensive language dataset compiled for Sinhala.
We also introduce SemiSOLD, a larger dataset containing more than 145,000
Sinhala tweets, annotated following a semi-supervised approach.
