Similarity measurement algorithms of writing and image for plagiarism on Facebook’s social media

Abstract

Currently, social media is growing very rapidly as a medium of information and interaction. However, these developments commonly not followed by a system that can detect the validity of the information also where the source came from, that resulting a negative potential such as fake news (hoaxes) or plagiarism. As a solution, in this research, will be testing plagiarism of writing and image on social media up by using URL as input test. Methods of measuring similarities that used in this research are Smith-Waterman Algorithm and Latent Semantic Analysis. The research measures the accuracy of both methods with local alignment and term-document approaches on documents and Facebook’s social media. The result of the research for Smith-Waterman Algorithm method with local alignment approach is better on document testing, with performance level up to 99.77%. In addition, in the proposed algorithm, image testing performed using Google Image Search can also indicate its relevance to a post. Thus, this algorithm has the opportunity to be develop again in other similarity searches such as authorship identification on a post as well as a hoax analyser.