The First Corpus for Detecting Fake News in Hausa Language

ABSTRACT

The rapid spread of deceptive news especially in Africa has become a global issue in last decade. This triggers the attention of the research community to develop efficient and reliable classification approaches for fake news detection so as to prevent its spread in the community. It has been explored that fake news in regional languages spread with a faster pace as compare to English language in local regions. Hausa is a very common language in Nigeria and some West African countries. So, it opens the challenge to detect the fake news in Hausa language. This paper presents the first corpus for the detection of fake news in Hausa. A dataset has been formed by collecting the labeled real and fake news consists of 2600 articles. In order to classify the fake news in Hausa, six different classifiers have been utilized. The performance of these approaches is then evaluated over different metrics and compared to determine the best model on the Hausa language dataset. The experimental results indicate that support vector machine (SVM) outperformed the other classifiers used by achieving 85% accuracy while AdaBoost happens to emerge as the fair model with 70% accuracy.