  In the Google Play store, an introduction page is associated with every
mobile application (app) for users to acquire its details, including
screenshots, description, reviews, etc. However, it remains a challenge to
identify what items influence users most when downloading an app. To explore
users' perspective, we conduct a survey to inquire about this question. The
results of survey suggest that the participants pay most attention to the app
description which gives users a quick overview of the app. Although there exist
some guidelines about how to write a good app description to attract more
downloads, it is hard to define a high quality app description. Meanwhile,
there is no tool to evaluate the quality of app description. In this paper, we
employ the method of crowdsourcing to extract the attributes that affect the
app descriptions' quality. First, we download some app descriptions from Google
Play, then invite some participants to rate their quality with the score from
one (very poor) to five (very good). The participants are also requested to
explain every score's reasons. By analyzing the reasons, we extract the
attributes that the participants consider important during evaluating the
quality of app descriptions. Finally, we train the supervised learning models
on a sample of 100 app descriptions. In our experiments, the support vector
machine model obtains up to 62% accuracy. In addition, we find that the
permission, the number of paragraphs and the average number of words in one
feature play key roles in defining a good app description.
