An Empirical Study of Bioinformatics Topics in Online Forum Discussions

Dibyendu Brinto Bose, Sheikh Hasib Ahmed, Gias Uddin, M. S. Rahman
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Abstract

In this paper, we aim to understand the topics discussed by bioinformatics practitioners in Stack Exchange sites. We downloaded all bioinformatics posts (questions and accepted answers) from four Stack Exchange Q&A sites (Stack Overflow, Biology, Cross-Validated, and Bioinformatics). Then we applied topic modeling on each site data. We labeled the topics and grouped those into high level categories. We analyzed the topics further by determining their popularity, difficulty, and evolution. We have made a comparative analysis of the topics across the different studied sites. We found 14 topics in Stack Overflow that are grouped into six categories. The number of new bioinformatics questions is steadily increasing over time in Stack Overflow for each topic category. Topics related to sequence analysis and pattern detection are the most popular as well as the most difficult to get an accepted answer. Most of the discussion posts are ‘how’ type questions, i.e., the practitioners were looking for solutions. While topics like biodata processing are found in multiple Stack Exchange sites, other topics (e.g., gene evolution analysis) are found in specialized sites (e.g., Biology). These findings show the need for consulting multiple related sites in Stack Exchange to learn interdisciplinary fields like bioinformatics. The tradeoff between popularity and difficulty of the bioinformatics topics highlights that bioinformatics practitioners need documentation and better tool support. The bioinformatics researchers, organizations, and practitioners can look into our results to prioritize the specific areas that need more focus for improvement.
网络论坛讨论中生物信息学主题的实证研究
在本文中,我们旨在了解生物信息学从业者在堆栈交换网站上讨论的主题。我们从四个Stack Exchange问答网站(Stack Overflow、Biology、Cross-Validated和bioinformatics)下载了所有的生物信息学帖子(问题和已接受的答案)。然后对每个站点数据应用主题建模。我们给主题打上标签,并把它们分成高级别的类别。我们通过确定主题的受欢迎程度、难度和演变来进一步分析这些主题。我们对不同研究地点的主题进行了比较分析。我们在Stack Overflow中发现了14个主题,分为6类。随着时间的推移,每个主题类别的堆栈溢出中新的生物信息学问题的数量正在稳步增加。与序列分析和模式检测相关的主题是最受欢迎的,也是最难得到公认答案的。大多数讨论帖都是“如何”类型的问题,也就是说,从业者在寻找解决方案。虽然像生物数据处理这样的主题可以在多个Stack Exchange站点中找到,但其他主题(例如,基因进化分析)可以在专门的站点中找到(例如,生物学)。这些发现表明,需要在Stack Exchange的多个相关网站上咨询,以了解生物信息学等跨学科领域。生物信息学主题的受欢迎程度和难度之间的权衡突出了生物信息学从业者需要文档和更好的工具支持。生物信息学研究人员、组织和从业者可以查看我们的结果,优先考虑需要更多关注改进的特定领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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