2023/04/26 by Jason Duran, Duran, Jason, Mostofa Sakib +5
Computer Science · Social Sciences · #Advanced Malware Detection Techniques #Big Data and Digital Economy #FOS: Computer and information sciences #Misinformation and Its Impacts #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2304.13769
openalex publication_date 2023/04/26 · openalex created_date 2023/04/30 · openalex updated_date 2026/07/28
The surge of research on fake news and misinformation in the aftermath of the 2016 election has led to a significant increase in publicly available source code repositories. Our study aims to systematically analyze and evaluate the most relevant repositories and their Python source code in this area to improve awareness, quality, and understanding of these resources within the research community. Additionally, our work aims to measure the quality and complexity metrics of these repositories and identify their fundamental features to aid researchers in advancing the fields knowledge in understanding and preventing the spread of misinformation on social media. As a result, we found that more popular fake news repositories and associated papers with higher citation counts tend to have more maintainable code measures, more complex code paths, a larger number of lines of code, a higher Halstead effort, and fewer comments.