  In Stack Overflow (SO), the quality of posts (i.e., questions and answers) is
subjectively evaluated by users through a voting mechanism. The net votes
(upvotes - downvotes) obtained by a post are often considered an approximation
of its quality. However, about half of the questions that received working
solutions got more downvotes than upvotes. Furthermore, about 18% of the
accepted answers (i.e., verified solutions) also do not score the maximum
votes. All these counter-intuitive findings cast doubts on the reliability of
the evaluation mechanism employed at SO. Moreover, many users raise concerns
against the evaluation, especially downvotes to their posts. Therefore,
rigorous verification of the subjective evaluation is highly warranted to
ensure a non-biased and reliable quality assessment mechanism. In this paper,
we compare the subjective assessment of questions with their objective
assessment using 2.5 million questions and ten text analysis metrics. According
to our investigation, four objective metrics agree with the subjective
evaluation, two do not agree, one either agrees or disagrees, and the remaining
three neither agree nor disagree with the subjective evaluation. We then
develop machine learning models to classify the promoted and discouraged
questions. Our models outperform the state-of-the-art models with a maximum of
about 76% - 87% accuracy.
