@InProceedings{lin-EtAl:2018:Long1,
  author    = {Lin, Bill Yuchen  and  Xu, Frank F.  and  Zhu, Kenny  and  Hwang, Seung-won},
  title     = {Mining Cross-Cultural Differences and Similarities in Social Media},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {709--719},
  abstract  = {Cross-cultural differences and similarities are common in cross-lingual natural language understanding, especially for research in social media. For instance, people of distinct cultures often hold different opinions on a single named entity. Also, understanding slang terms across languages requires knowledge of cross-cultural similarities. In this paper, we study the problem of computing such cross-cultural differences and similarities. We present a lightweight yet effective approach, and evaluate it on two novel tasks: 1) mining cross-cultural differences of named entities and 2) finding similar terms for slang across languages. Experimental results show that our framework substantially outperforms a number of baseline methods on both tasks. The framework could be useful for machine translation applications and research in computational social science.},
  url       = {http://www.aclweb.org/anthology/P18-1066}
}