  Online harassment is a significant social problem. Prevention of online
harassment requires rapid detection of harassing, offensive, and negative
social media posts. In this paper, we propose the use of word embedding models
to identify offensive and harassing social media messages in two aspects:
detecting fast-changing topics for more effective data collection and
representing word semantics in different domains. We demonstrate with
preliminary results that using the GloVe (Global Vectors for Word
Representation) model facilitates the discovery of new and relevant keywords to
use for data collection and trolling detection. Our paper concludes with a
discussion of a research agenda to further develop and test word embedding
models for identification of social media harassment and trolling.
