TY - GEN
T1 - Evaluating Code Metrics in GitHub Repositories Related to Fake News and Misinformation
AU - Duran, Jason
AU - Sakib, Mostofa Najmus
AU - Eisty, Nasir U.
AU - Spezzano, Francesca
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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 field's 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. Utilizing these findings to devise efficient research and coding techniques to combat fake news, we can strive towards building a more knowledgeable and well-informed society.
AB - 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 field's 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. Utilizing these findings to devise efficient research and coding techniques to combat fake news, we can strive towards building a more knowledgeable and well-informed society.
KW - Code Metrics
KW - Fake News
KW - GitHub Repository Mining
KW - Misinformation
KW - Source Code Analysis
UR - http://www.scopus.com/inward/record.url?scp=85168775837&partnerID=8YFLogxK
U2 - 10.1109/SERA57763.2023.10197739
DO - 10.1109/SERA57763.2023.10197739
M3 - Conference contribution
AN - SCOPUS:85168775837
T3 - Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023
SP - 182
EP - 188
BT - Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023
A2 - Song, Yeong-Tae
A2 - Rhee, Junghwan
A2 - Jeon, Yuseok
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023
Y2 - 23 May 2023 through 25 May 2023
ER -