  In community question-answering platforms, tags play essential roles in
effective information organization and retrieval, better question routing,
faster response to questions, and assessment of topic popularity. Hence,
automatic assistance for predicting and suggesting tags for posts is of high
utility to users of such platforms. To develop better tag prediction across
diverse communities and domains, we performed a thorough analysis of users'
tagging behavior in 17 StackExchange communities. We found various common
inherent properties of this behavior in those diverse domains. We used the
findings to develop a flexible neural tag prediction architecture, which
predicts both popular tags and more granular tags for each question. Our
extensive experiments and obtained performance show the effectiveness of our
model
