@InProceedings{takeuchi-EtAl:2017:DDDSM,
  author    = {Takeuchi, Ryo  and  ISO, Hayate  and  Ito, Kaoru  and  Wakamiya, Shoko  and  Aramaki, Eiji},
  title     = {Multivariate Linear Regression of Symptoms-related Tweets for Infectious Gastroenteritis Scale Estimation},
  booktitle = {Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Association for Computational Linguistics},
  pages     = {18--25},
  abstract  = {To date, various Twitter-based event detection systems have been proposed.
	Most of their targets, however, share common characteristics. They are seasonal
	or global events such as earthquakes and flu pandemics.
	In contrast, this study targets unseasonal and local disease events.
	Our system investigates the frequencies of disease-related words such as
	"nausea","chill",and "diarrhea" and estimates the number of patients using
	regression of these word frequencies.
	Experiments conducted using Japanese 47 areas from January 2017 to April 2017
	revealed that the detection of small and unseasonal event is extremely
	difficult (overall performance: 0.13).
	However, we found that the event scale and the detection performance show high
	correlation in the specified cases (in the phase of patient increasing or
	decreasing).
	The results also suggest that when 150 and more patients appear in a high
	population area, we can expect that our social sensors detect this outbreak.
	Based on these results, we can infer that social sensors can reliably detect
	unseasonal and local disease events under certain conditions, just as they can
	for seasonal or global events.},
  url       = {http://www.aclweb.org/anthology/W17-5803}
}