  Along with the COVID-19 pandemic, an "infodemic" of false and misleading
information has emerged and has complicated the COVID-19 response efforts.
Social networking sites such as Facebook and Twitter have contributed largely
to the spread of rumors, conspiracy theories, hate, xenophobia, racism, and
prejudice. To combat the spread of fake news, researchers around the world have
and are still making considerable efforts to build and share COVID-19 related
research articles, models, and datasets. This paper releases "AraCOVID19-MFH" a
manually annotated multi-label Arabic COVID-19 fake news and hate speech
detection dataset. Our dataset contains 10,828 Arabic tweets annotated with 10
different labels. The labels have been designed to consider some aspects
relevant to the fact-checking task, such as the tweet's check worthiness,
positivity/negativity, and factuality. To confirm our annotated dataset's
practical utility, we used it to train and evaluate several classification
models and reported the obtained results. Though the dataset is mainly designed
for fake news detection, it can also be used for hate speech detection,
opinion/news classification, dialect identification, and many other tasks.
