Detection of FLOSS version release events from Stack Overflow message data.
Artur Sokolovsky, Thomas Groß, Jaume Bacardit · arXiv (Cornell University) · 2020
Topic Detection and Tracking (TDT) is a very active research question within the area of text mining, generally applied to news feeds and Twitter datasets, where topics and events are detected. The notion of event is broad, but typically it applies to occurrences that can be detected from a single post or a message. Little attention has been drawn to what we call micro-events, which, due to their nature, cannot be detected from a single piece of textual information. The study investigates micro-event detection on textual data using a sample of messages from the Stack Overflow Q&A platform in order to detect Free/Libre Open Source Software (FLOSS) version releases. Micro-events are detected using logistic regression models with step-wise forward regression feature selection from a set of LDA topics and sentiment analysis features. We perform a detailed statistical analysis of the models, including influential cases, variance inflation factors, validation of the linearity assumption, pseudo R sq. measures and no-information rate. Finally, in order to understand the detection limits and improve the performance of the estimators, we suggest a method for generating micro-event synthetic datasets and use them identify the micro-event detectability thresholds.