  Text actionability detection is the problem of classifying user authored
natural language text, according to whether it can be acted upon by a
responding agent. In this paper, we propose a supervised learning framework for
domain-aware, large-scale actionability classification of social media
messages. We derive lexicons, perform an in-depth analysis for over 25 text
based features, and explore strategies to handle domains that have limited
training data. We apply these methods to over 46 million messages spanning 75
companies and 35 languages, from both Facebook and Twitter. The models achieve
an aggregate population-weighted F measure of 0.78 and accuracy of 0.74, with
values of over 0.9 in some cases.
