News Reliability Evaluation using Latent Semantic Analysis

Guo Xiaoning, Tan De Zhern, Soo Wooi King, Tan Yi Fei, Lam Hai Shuan


The rapid rise and widespread of ‘Fake News’ has severe implications in the society today. Much efforts have been directed towards the development of methods to verify news reliability on the Internet in recent years. In this paper, an automated news reliability evaluation system was proposed. The system utilizes term several Natural Language Processing (NLP) techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), Phrase Detection and Cosine Similarity in tandem with Latent Semantic Analysis (LSA). A collection of 9203 labelled articles from both reliable and unreliable sources were collected. This dataset was then applied random test-train split to create the training dataset and testing dataset. The final results obtained shows 81.87% for precision and 86.95% for recall with the accuracy being 73.33%.


fake news detection; natural language processing; latent semantic analysis; cosine similarity; tf-idf;

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