The following abstract is from a student co-authored paper, “Approaches in Fake News Detection: An Evaluation of Natural Language Processing and Machine Learning Techniques on the Reddit Social Network,” presented by Dr. Jason Isaacs at the 8th International Conference on Data Mining and Applications (DMA 2022), presented on May 28, 2022.
Abstract
Classifier algorithms are a subfield of data mining and play an integral role in finding patterns and relationships within large datasets. In recent years, fake news detection has become a popular area of data mining for several important reasons, including its negative impact on decision-making and its virality within social networks. In the past, traditional fake news detection has relied primarily on information context, while modern approaches rely on auxiliary information to classify content. Modelling with machine learning and natural language processing can aid in distinguishing between fake and real news. In this research, we mine data from Reddit, the popular online discussion forum and social news aggregator, and measure machine learning classifiers in order to evaluate each algorithm’s accuracy in detecting fake news using only a minimal subset of data.
Reference
M. Shariff, B. Thoms, J. Isaacs, “Approaches in Fake News Detection: An Evaluation of Natural Language Processing and Machine Learning Techniques on the Reddit Social Network,” Accepted for inclusion in 8th International Conference on Data Mining and Applications (DMA 2022), May 28-29, 2022.