Highlighting the importance of semantics in COVID-19 fake news detection using a CNN hashmap color-based method

With the exponential development and exploitation of social media sites and platforms such as facebook, twitter and instagram, a diversity type of news are reached to the users,resulting in a major influence on human health and safety.Spreading misinformation and disinformation during the Covid-19 pandemic has become increasingly significant. Although it is usually not a criminal act, it can cause serious endangerment to public health. Such infodemic movement is often lead to advance geopolitical interests by the states, to achieve some sort of profit by some opportunists and individuals or discredit official sources. Hence,it has become crucial to automate the detection of fake news in order to shield people from any harmful repercussions. In this paper, the importance of semantics in Covid-19 fake news detection is highlighted based on a convolutional neural network classifier and a hashmap color-based technique. The experiments are performed with CoAID(Covid-19 heAlthcare mIsinformation Dataset),and the results prove that the loss of semantics yields to a poor performance of the classifier. This implicates additional constraints to the training images,with focus on creating a CNN-based color hashmap classifier that includes anterior and posterior neighbors.