@InProceedings{martin:2017:NLPandCSS,
  author    = {Martin, Trevor},
  title     = {community2vec: Vector representations of online communities encode semantic relationships},
  booktitle = {Proceedings of the Second Workshop on NLP and Computational Social Science},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {27--31},
  abstract  = {Vector embeddings of words have been shown to encode meaningful semantic
	relationships that enable solving of complex analogies. This vector embedding
	concept has been extended successfully to many different domains and in this
	paper we both create and visualize vector representations of an unstructured
	collection of online communities based on user participation. Further, we
	quantitatively and qualitatively show that these representations allow solving
	of semantically meaningful community analogies and also other more general
	types of relationships. These results could help improve community
	recommendation engines and also serve as a tool for sociological studies of
	community relatedness.},
  url       = {http://www.aclweb.org/anthology/W17-2904}
}