Temporal Analysis of Literary and Programming Prose
This work provides a fast prototyping method for temporal analysis in literary and programming domains, though it appears incremental as it applies existing tools to new data types.
The paper tackles the problem of analyzing temporal patterns in literary works and programming code, using Google n-gram viewer for literary prose and statistical analysis on software commit logs, finding that certain time periods like Sunday are disproportionately referenced and reinforcing or weakening beliefs about college students' open-source contributions.
Literary works reference a variety of globally shared themes including well-known people, events, and time periods. It is particularly interesting to locate patterns that are either invariant across time or exhibit a characteristic change across time, as they could imply something important about society that those works record. This paper suggests the use of Google n-gram viewer as a fast prototyping method for examining time-based properties over a rich sample of literary prose. Using this method, we find that some repeating periods of time, like Sunday, are referenced disproportionally, allowing us to pose questions such as why a day like Thursday is so unpopular. Furthermore, by treating software as a work of prose, we can apply a similar analysis to open-source software repositories and explore time-based relations in commit logs. Doing a simple statistical analysis on a few temporal keywords in the log records, we reinforce and weaken a few beliefs on how college students approach open source software. Finally, we help readers working on their own temporal analysis by comparing the fundamental differences between literary works and code repositories, and suggest blog or wiki as recently-emerging works.