  Development bots are used on Github to automate repetitive activities. Such
bots communicate with human actors via issue comments and pull request
comments. Identifying such bot comments allows preventing bias in
socio-technical studies related to software development. To automate their
identification, we propose a classification model based on natural language
processing. Starting from a balanced ground-truth dataset of 19,282 PR and
issue comments, we encode the comments as vectors using a combination of the
bag of words and TF-IDF techniques. We train a range of binary classifiers to
predict the type of comment (human or bot) based on this vector representation.
A multinomial Naive Bayes classifier provides the best results. Its performance
on a test set containing 50% of the data achieves an average precision, recall,
and F1 score of 0.88. Although the model shows a promising result on the pull
request and issue comments, further work is required to generalize the model on
other types of activities, like commit messages and code reviews.
