DEVELOPMENT OF A CONCEPTUAL FRAMEWORK AND A MEASUREMENT MODEL FOR THE DETECTION OF FAKE NEWS

Fake news has been there since before the advent of the Internet. It has had an immense impact on our modern society. Detecting fake news is an important step. Although there are various ways and methods in which fake news can be detected and solved. In this research paper we discuss the various conceptual frameworks and how they affect fake news. It further shows the development of the conceptual framework and the measurement model used; showing which of the frameworks fake news is most likely to surface through. The objective of the research is to design a conceptual framework for fake news detection, whereby developing measurement model for fake news detection, and the framework and model are evaluated for fake news detection. Fake news detection approaches can be divided as: creator and user features, news content features and social context features. A survey was taken based on this feature via questionnaire to determine in which feature, fake news can be quickly spotted. Results: Results shows that fake news can be easily spotted in the creator and user feature, this feature was then used to perform a feature selection on a fake news dataset which gave better accuracy.