Details
| Original language | English |
|---|---|
| Title of host publication | ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media |
| Publication status | Published - 2012 |
| Event | 6th International AAAI Conference on Weblogs and Social Media, ICWSM 2012 - Dublin, Ireland Duration: 4 Jun 2012 → 7 Jun 2012 |
Publication series
| Name | ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media |
|---|
Abstract
Tracking Twitter for public health has shown great potential. However, most recent work has been focused on correlating Twitter messages to influenza rates, a disease that exhibits a marked seasonal pattern. In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capacity? In May 2011, Germany reported an outbreak of Enterohemorrhagic Escherichia coli (EHEC). It was one of the largest described outbreaks of EHEC/HUS worldwide and the largest in Germany. In this work, we study the crowd's behavior in Twitter during the outbreak. In particular, we report how tracking Twitter helped to detect key user messages that triggered signal detection alarms before MedISys and other well established early warning systems. We also introduce a personalized learning to rank approach that exploits the relationships discovered by: (i) latent semantic topics computed using Latent Dirichlet Allocation (LDA), and (ii) observing the social tagging behavior in Twitter, to rank tweets for epidemic intelligence. Our results provide the grounds for new public health research based on social media.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
Sustainable Development Goals
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ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media. 2012. (ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Epidemic Intelligence for the Crowd, by the Crowd
AU - Diaz-Aviles, Ernesto
AU - Stewart, Avaré
AU - Velasco, Edward
AU - Denecke, Kerstin
AU - Nejdl, Wolfgang
N1 - Short version
PY - 2012
Y1 - 2012
N2 - Tracking Twitter for public health has shown great potential. However, most recent work has been focused on correlating Twitter messages to influenza rates, a disease that exhibits a marked seasonal pattern. In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capacity? In May 2011, Germany reported an outbreak of Enterohemorrhagic Escherichia coli (EHEC). It was one of the largest described outbreaks of EHEC/HUS worldwide and the largest in Germany. In this work, we study the crowd's behavior in Twitter during the outbreak. In particular, we report how tracking Twitter helped to detect key user messages that triggered signal detection alarms before MedISys and other well established early warning systems. We also introduce a personalized learning to rank approach that exploits the relationships discovered by: (i) latent semantic topics computed using Latent Dirichlet Allocation (LDA), and (ii) observing the social tagging behavior in Twitter, to rank tweets for epidemic intelligence. Our results provide the grounds for new public health research based on social media.
AB - Tracking Twitter for public health has shown great potential. However, most recent work has been focused on correlating Twitter messages to influenza rates, a disease that exhibits a marked seasonal pattern. In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capacity? In May 2011, Germany reported an outbreak of Enterohemorrhagic Escherichia coli (EHEC). It was one of the largest described outbreaks of EHEC/HUS worldwide and the largest in Germany. In this work, we study the crowd's behavior in Twitter during the outbreak. In particular, we report how tracking Twitter helped to detect key user messages that triggered signal detection alarms before MedISys and other well established early warning systems. We also introduce a personalized learning to rank approach that exploits the relationships discovered by: (i) latent semantic topics computed using Latent Dirichlet Allocation (LDA), and (ii) observing the social tagging behavior in Twitter, to rank tweets for epidemic intelligence. Our results provide the grounds for new public health research based on social media.
UR - http://www.scopus.com/inward/record.url?scp=84928108083&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84928108083
SN - 9781577355564
T3 - ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media
BT - ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media
T2 - 6th International AAAI Conference on Weblogs and Social Media, ICWSM 2012
Y2 - 4 June 2012 through 7 June 2012
ER -