Detail publikace

Finding Indicators of Epidemiological Events by Analyzing Messages from Twitter and Other Social Media

Originální název

Finding Indicators of Epidemiological Events by Analyzing Messages from Twitter and Other Social Media

Anglický název

Finding Indicators of Epidemiological Events by Analyzing Messages from Twitter and Other Social Media

Jazyk

en

Originální abstrakt

This paper deals with early detection of epidemiological events by means of text-based analysis on social networks data. We introduce a novel system that processes streams from Twitter, blogs and discussion fora, automatically categorizes messages according to various criteria and extracts data potentially relevant for public health-related events. A special attention is paid to the analysis of Twitter data. We quantify the data processed every day and show that many obstacles need to be overcome to fully realize the potential of the valuable resource.

Anglický abstrakt

This paper deals with early detection of epidemiological events by means of text-based analysis on social networks data. We introduce a novel system that processes streams from Twitter, blogs and discussion fora, automatically categorizes messages according to various criteria and extracts data potentially relevant for public health-related events. A special attention is paid to the analysis of Twitter data. We quantify the data processed every day and show that many obstacles need to be overcome to fully realize the potential of the valuable resource.

BibTex


@inproceedings{BUT76491,
  author="Lubomír {Otrusina} and Pavel {Smrž}",
  title="Finding Indicators of Epidemiological Events by Analyzing Messages from Twitter and Other Social Media",
  annote="This paper deals with early detection of epidemiological events by means of
text-based analysis on social networks data. We introduce a novel system that
processes streams from Twitter, blogs and discussion fora, automatically
categorizes messages according to various criteria and extracts data potentially
relevant for public health-related events. A special attention is paid to the
analysis of Twitter data. We quantify the data processed every day and show that
many obstacles need to be overcome to fully realize the potential of the valuable
resource.",
  address="Association for Computing Machinery",
  booktitle="20th ACM Conference on Information and Knowledge Management workshop proceedings by ACM",
  chapter="76491",
  edition="NEUVEDEN",
  howpublished="print",
  institution="Association for Computing Machinery",
  year="2011",
  month="october",
  pages="1--4",
  publisher="Association for Computing Machinery",
  type="conference paper"
}