Details
| Original language | English |
|---|---|
| Title of host publication | WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion |
| Pages | 495-496 |
| Number of pages | 2 |
| Publication status | Published - 16 Apr 2012 |
| Event | 21st Annual Conference on World Wide Web, WWW'12 - Lyon, France Duration: 16 Apr 2012 → 20 Apr 2012 |
Publication series
| Name | WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion |
|---|
Abstract
In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capabilities? In May 2011, Germany reported one of the largest described outbreaks of Enterohemorrhagic Escherichia coli (EHEC). By end of June, 47 persons had died. After the detection of the outbreak, authorities investigating the cause and the impact in the population were interested in the analysis of micro-blog data related to the event. Since Thousands of tweets related to this outbreak were produced every day, this task was overwhelming for experts participating in the investigation. In this work, we propose a Personalized Tweet Ranking algorithm for Epidemic Intelligence (PTR4EI), that provides users a personalized, short list of tweets based on the user's context. PTR4EI is based on a learning to rank framework and exploits as features, complementary context information extracted from the social hashtagging behavior in Twitter. Our experimental evaluation on a dataset, collected in real-time during the EHEC outbreak, shows the superior ranking performance of PTR4EI. We believe our work can serve as a building block for an open early warning system based on Twitter, helping to realize the vision of Epidemic Intelligence for the Crowd, by the Crowd. Copyright is held by the author/owner(s).
Keywords
- Learning to rank, Recommender systems, Twitter
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
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WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion. 2012. p. 495-496 (WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Towards Personalized Learning to Rank for Epidemic Intelligence Based on Social Media Streams
AU - Diaz-Aviles, Ernesto
AU - Stewart, Avaré
AU - Velasco, Edward
AU - Denecke, Kerstin
AU - Nejdl, Wolfgang
PY - 2012/4/16
Y1 - 2012/4/16
N2 - In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capabilities? In May 2011, Germany reported one of the largest described outbreaks of Enterohemorrhagic Escherichia coli (EHEC). By end of June, 47 persons had died. After the detection of the outbreak, authorities investigating the cause and the impact in the population were interested in the analysis of micro-blog data related to the event. Since Thousands of tweets related to this outbreak were produced every day, this task was overwhelming for experts participating in the investigation. In this work, we propose a Personalized Tweet Ranking algorithm for Epidemic Intelligence (PTR4EI), that provides users a personalized, short list of tweets based on the user's context. PTR4EI is based on a learning to rank framework and exploits as features, complementary context information extracted from the social hashtagging behavior in Twitter. Our experimental evaluation on a dataset, collected in real-time during the EHEC outbreak, shows the superior ranking performance of PTR4EI. We believe our work can serve as a building block for an open early warning system based on Twitter, helping to realize the vision of Epidemic Intelligence for the Crowd, by the Crowd. Copyright is held by the author/owner(s).
AB - In the presence of sudden outbreaks, how can social media streams be used to strengthen surveillance capabilities? In May 2011, Germany reported one of the largest described outbreaks of Enterohemorrhagic Escherichia coli (EHEC). By end of June, 47 persons had died. After the detection of the outbreak, authorities investigating the cause and the impact in the population were interested in the analysis of micro-blog data related to the event. Since Thousands of tweets related to this outbreak were produced every day, this task was overwhelming for experts participating in the investigation. In this work, we propose a Personalized Tweet Ranking algorithm for Epidemic Intelligence (PTR4EI), that provides users a personalized, short list of tweets based on the user's context. PTR4EI is based on a learning to rank framework and exploits as features, complementary context information extracted from the social hashtagging behavior in Twitter. Our experimental evaluation on a dataset, collected in real-time during the EHEC outbreak, shows the superior ranking performance of PTR4EI. We believe our work can serve as a building block for an open early warning system based on Twitter, helping to realize the vision of Epidemic Intelligence for the Crowd, by the Crowd. Copyright is held by the author/owner(s).
KW - Learning to rank
KW - Recommender systems
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84861052088&partnerID=8YFLogxK
U2 - 10.1145/2187980.2188094
DO - 10.1145/2187980.2188094
M3 - Conference contribution
AN - SCOPUS:84861052088
SN - 9781450312301
T3 - WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
SP - 495
EP - 496
BT - WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
T2 - 21st Annual Conference on World Wide Web, WWW'12
Y2 - 16 April 2012 through 20 April 2012
ER -