Microblog-based Infectious Disease Detection using Document Classification and Infectious Disease Model
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- Aramaki Eiji
- PRESTO (Precursory Research for Embryonic Science and Technology) Center for Knowledge Structuring, The University of Tokyo
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- Masukawa Sachiko
- Center for Knowledge Structuring, The University of Tokyo
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- Morita Mizuki
- NIBIO (The National Institute of Biomedical Innovation) Center for Knowledge Structuring, The University of Tokyo
Bibliographic Information
- Other Title
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- 文章分類と疾患モデルの融合によるソーシャルメディアからの感染症把握
- ブンショウ ブンルイ ト シッカン モデル ノ ユウゴウ ニ ヨル ソーシャル メディア カラ ノ カンセンショウ ハアク
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Abstract
With the recent rise in popularity and size of social media, there is a growing need for systems that can extract useful information from this amount of data. We address an issue of detecting influenza epidemics. Although previous methods rely mainly on the frequencies of the influenza related words, such methods had suffered from the noisy tweets that do not express influenza symptoms. To deal with this problem, this study proposed two methods. First, the sentence classifier judges whether a person really catches the influenza or not. Next, the infectious model closes a time gap between the people web activity and the illness period. In the experiments, the combination of two techniques achieved the high performance (correlation coefficient 0.910 to the number of the influenza patients). This result suggests that not only natural language processing but also disease study contributes to social media based surveillance.
Journal
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- Journal of Natural Language Processing
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Journal of Natural Language Processing 19 (5), 419-435, 2012
The Association for Natural Language Processing
- Tweet
Details 詳細情報について
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- CRID
- 1390282679451722496
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- NII Article ID
- 10031134264
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- NII Book ID
- AN10472659
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- ISSN
- 21858314
- 13407619
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- NDL BIB ID
- 024167561
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed