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Misinformation has become a major concern in recent last years given itsspread across our information sources. In the past years, many NLP tasks havebeen introduced in this area, with some systems reaching good results onEnglish language datasets. Existing AI based approaches for fightingmisinformation in literature suggest automatic stance detection as an integralfirst step to success. Our paper aims at utilizing this progress made forEnglish to transfers that knowledge into other languages, which is anon-trivial task due to the domain gap between English and the targetlanguages. We propose a black-box non-intrusive method that utilizes techniquesfrom Domain Adaptation to reduce the domain gap, without requiring any humanexpertise in the target language, by leveraging low-quality data in both asupervised and unsupervised manner. This allows us to rapidly achieve similarresults for stance detection for the Zulu language, the target language in thiswork, as are found for English. We also provide a stance detection dataset inthe Zulu language. Our experimental results show that by leveraging Englishdatasets and machine translation we can increase performances on both Englishdata along with other languages.

Bridging the Domain Gap for Stance Detection for the Zulu language