Implementing Fact-Checking in Journalistic Articles Shared on Social Media in the Philippines Using Knowledge Graphs

Abstract

In the technology age, articles with fraudulent content are rampant, especially articles shared on social media. Misinformation could just be an inaccuracy at its best, or it could lead to normalizing false information at worst. To aid the predicament, the researchers created a system that will “fact check” suspicious articles against those articles that have been deemed credible, reliable, and more accurate, in order to help fight deceiving content that may be detrimental to society. The journal regarding computational fact checking that was published by Ciampaglia, et. al. (2015) from the Indiana University in the USA entitled Computational Fact Checking from Knowledge Networks, was used as the basis and inspiration for this thesis. The researchers made use of the undirected graph (UG) together with a part-of-speech (POS) tagging algorithm to create a knowledge graph (KG) that would serve as the center of the system. Five different POS tagging algorithms were paired with the UG to assess which combination would yield the best results, these are Conditional Random Fields, Logistic Regression, a Hybrid of CRF and LR, Random Forests, and K-Nearest Neighbors. Random Forests and K-Nearest Neighbors were classification algorithms used in Ciampaglia's study. It was concluded that among the 5 pairs of UG and POS Tagging algorithms, the Hybrid of CRF and LR used as a POS tagger, together with the UG, created the most efficient KG.