Evaluating fake news detection models and associated biases in IoT

Long Qian; Hao Qian; Yangyu Chen

doi:10.1117/12.2646927

7 September 2022 Evaluating fake news detection models and associated biases in IoT

Long Qian, Hao Qian, Yangyu Chen

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 1232926 (2022) https://doi.org/10.1117/12.2646927
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Fake news has always been a problem for online communities. It can build mistrust, create boundaries between people, and lower productivity in many government departments. Distinguishing fake news by human is slow and expensive, therefore, machines were proposed by many scholars to do the job. There are mature machine learning algorithms available to use for distinguishing fake news and real news. Despite the high accuracy of most well designed machine learning models, they can still make mistakes on some news. In this paper, we compared three machine learning models for news classification and evaluate biases in these models. The first one is Embedding Bag, second one is CNN network, and then the Naive Bayes Classifier. After tidying and vectorizing the data, they were feed to these classifiers to get the model. As a result, all of the models achieved very high accuracy. All of them had an accuracy of higher than 90 percent. The biases for all of the approaches are primarily sampling biases. The less represented words have higher chances to be mis classified than those more represented words in the database.

Citation Download Citation

Long Qian, Hao Qian, and Yangyu Chen "Evaluating fake news detection models and associated biases in IoT", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 1232926 (7 September 2022); https://doi.org/10.1117/12.2646927

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