  In this paper, we address the problem of detecting expressions of moral
values in tweets using content analysis. This is a particularly challenging
problem because moral values are often only implicitly signaled in language,
and tweets contain little contextual information due to length constraints. To
address these obstacles, we present a novel approach to automatically acquire
background knowledge from an external knowledge base to enrich input texts and
thus improve moral value prediction. By combining basic text features with
background knowledge, our overall context-aware framework achieves performance
comparable to a single human annotator. To the best of our knowledge, this is
the first attempt to incorporate background knowledge for the prediction of
implicit psychological variables in the area of computational social science.
