@InProceedings{nand-perera-kasture:2016:COLING,
  author    = {Nand, Parma  and  Perera, Rivindu  and  Kasture, Abhijeet},
  title     = {"How Bullying is this Message?": A Psychometric Thermometer for Bullying},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {695--706},
  abstract  = {Cyberbullying statistics are shocking, the number of affected young people is
	increasing dramatically with the affordability of mobile technology devices
	combined with a growing number of social networks. This paper proposes a
	framework to analyse Tweets with the goal to identify cyberharassment in social
	networks as an important step to protect people from cyberbullying. The
	proposed framework incorporates latent or hidden variables with supervised
	learning to determine potential bullying cases resembling short blogging type
	texts such as Tweets. It uses the LIWC2007 - tool that translates Tweet
	messages into 67 numeric values, representing 67 word categories. The output
	vectors are then used as features for four different classifiers implemented in
	Weka. Tests on all four classifiers delivered encouraging predictive capability
	of Tweet messages.  Overall it was found that the use of numeric psychometric
	values outperformed the same algorithms using both filtered and unfiltered
	words as features.  The best performing algorithms was Random Forest with an
	F1-value of 0.947 using psychometric features compared to a value of 0.847 for
	the same algorithm using words as features.},
  url       = {http://aclweb.org/anthology/C16-1067}
}