A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow

• URL: http://arxiv.org/abs/2004.09495v1
• Date: Sat, 18 Apr 2020 08:19:46 GMT
• Title: A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow
• Authors: Konstantinos Georgiou, Nikolaos Mittas, Lefteris Angelis, Alexander Chatzigeorgiou
• Abstract summary: Study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining. Findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data.
• Score: 69.5231754305538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Covid-19 outbreak, beyond its tragic effects, has changed to an unprecedented extent almost every aspect of human activity throughout the world. At the same time, the pandemic has stimulated enormous amount of research by scientists across various disciplines, seeking to study the phenomenon itself, its epidemiological characteristics and ways to confront its consequences. Information Technology, and particularly Data Science, drive innovation in all related to Covid-19 biomedical fields. Acknowledging that software developers routinely resort to open question and answer communities like Stack Overflow to seek advice on solving technical issues, we have performed an empirical study to investigate the extent, evolution and characteristics of Covid-19 related posts. In particular, through the study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining, we attempt to shed light into the interest of developers in Covid-19 related topics and the most popular technological problems for which the users seek information. The findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data, primarily using Python technologies.

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