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The surge of research on fake news and misinformation in the aftermath of the2016 election has led to a significant increase in publicly available sourcecode repositories. Our study aims to systematically analyze and evaluate themost relevant repositories and their Python source code in this area to improveawareness, quality, and understanding of these resources within the researchcommunity. Additionally, our work aims to measure the quality and complexitymetrics of these repositories and identify their fundamental features to aidresearchers in advancing the fields knowledge in understanding and preventingthe spread of misinformation on social media. As a result, we found that morepopular fake news repositories and associated papers with higher citationcounts tend to have more maintainable code measures, more complex code paths, alarger number of lines of code, a higher Halstead effort, and fewer comments.

Evaluating Code Metrics in GitHub Repositories Related to Fake News and Misinformation