@InProceedings{ambroselli-EtAl:2018:N18-3,
  author    = {Ambroselli, Carl  and  Risch, Julian  and  Krestel, Ralf  and  Loos, Andreas},
  title     = {Prediction for the Newsroom: Which Articles Will Get the Most Comments?},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans - Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {193--199},
  abstract  = {The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time. But the same success also endangers this freedom of speech due to closing down of participatory sites misused by individuals or interest groups. We propose to support manual moderation by proactively drawing the attention of our moderators to article discussions that most likely need their intervention. To this end, we predict which articles will receive a high number of comments. In contrast to existing work, we enrich the article with metadata, extract semantic and linguistic features, and exploit annotated data from a foreign language corpus. Our logistic regression model improves F1-scores by over 80% in comparison to state-of-the-art approaches.},
  url       = {http://www.aclweb.org/anthology/N18-3024}
}