Of which has become an important criterion for quality and reliability. In response to the global economic crisis, we have developed a benchmark for fake news. We call it “FAKE” and it is based on the assumed absence of a context and the easy availability of online information, which promotes the emergence of a pack of sensationalist but baseless messages. This provides a convenient marker for quality assessment, while standardizing and quantifying the quality of media content.
This would provide crucial evidence for improving the trustworthiness of the news. In addition, it could be applied in domains that lack an established vocabulary and reference point, as it is not bound by the selection of certain terms such as “fake news”, but would automatically make use of the provided data.
The “Fake” tool will help to reduce the production of fake news. This can be achieved by adding a low tolerance for malicious practices such as word salad, and focus on the quality of the information itself rather than the authors and the consumers. It will also help in building a more reliable interrelationship of people and data.
The “Fake” tool is made equal to the original in terms of data reliability and meaningfulness. This requires us to close the gap without being bound by the traditional definitions of the word “fake”” and “false media”. It leads to a more precise differentiation between newsworthy content and biased content. Through the analysis of the content, we provide the tool for automated classification of the statements into reliable and non-reliable pieces of information.
The user is required to provide the required label, and the tool seeks to classify the provided information into an optimal classification scheme, which usually includes true, false, false, false and false statements. The course of action only happens when all false statements are excluded. d2c66b5586