Model Stitching Algorithm for Fake News Detection Problem

Nowadays, we can see how social media networks are developing. We must accept the fact that the opinion of an expert is frequently just as valuable and crucial as that of a non-expert. It is feasible to see how traditional media is undergoing changes and processes that diminish the importance of the traditional ”editing office” and place a growing focus on journalists’ remote labour.As a result, social media has evolved into a component of national security since fake news and disinformation spread by nefarious individuals can influence readers and spark pointless debates on social issues that are inherently unimportant. This has a domino effect, instils dread in the populace, and eventually puts the security of the state in jeopardy.Recently, deep machine learning techniques have proven to be one of the technologies thought to be an effective way to combat the false news problem. However, due to shortages of labelled data, these methods often have poor model generalization capabilities when applied in real-world cases.In this paper, we address this problem by utilizing lightweight model stitching, which serves as a foundation for a hybrid method for fake news detection. Six distinct benchmark datasets have been used in our varied experiments. The outcomes are promising and pave the way for additional studies.