ABSTRACT
The impact of technology on people's lives has grown continuously. The consumption of online news is one of the important trends as the share of population with internet access grows rapidly over time. Global statistics have shown that the internet and social media usage has an increasing trend. Recent developments like the Covid 19 pandemic have amplified this trend even more. However, the credibility of online news is a very critical issue to consider since it directly impacts the society and the people's mindsets. Majority of users tend to instinctively believe what they encounter and come into conclusions based upon them. It is essential that the consumers have an understanding or prior knowledge regarding the news and its source before coming into conclusions. This research proposes a hybrid model to predict the accuracy of a particular news article in Sinhala text. The model combines the general news content based analysis techniques using machine learning/ deep learning classifiers with social network related features of the news source to make predictions. A scoring mechanism is utilized to provide an overall score to a given news item where two independent scores- Accuracy Score (by analyzing the news content) and Credibility Score (by a scoring mechanism on social network features of the news source) are combined. The hybrid model containing the Passive Aggressive Classifier has shown the highest accuracy of 88%. Also, the models containing deep neural netWorks has shown accuracy around 75-80%. These results highlight that the proposed method could efficiently serve as a Fake News Detection mechanism for news content in Sinhala Language. Also, since there's no publicly available dataset for Fake News detection in Sinhala, the datasets produced in this work could also be considered as a contribution from this research. © 2022 IEEE.