Influenza-like illness surveillance on Twitter through automated learning of naïve language.
Twitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using naïve, everyday language. We developed a minimally trained algor...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3853203?pdf=render |
| _version_ | 1818059800223678464 |
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| author | Francesco Gesualdo Giovanni Stilo Eleonora Agricola Michaela V Gonfiantini Elisabetta Pandolfi Paola Velardi Alberto E Tozzi |
| author_facet | Francesco Gesualdo Giovanni Stilo Eleonora Agricola Michaela V Gonfiantini Elisabetta Pandolfi Paola Velardi Alberto E Tozzi |
| author_sort | Francesco Gesualdo |
| collection | DOAJ |
| description | Twitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using naïve, everyday language. We developed a minimally trained algorithm that exploits the abundance of health-related web pages to identify all jargon expressions related to a specific technical term. We then translated an influenza case definition into a Boolean query, each symptom being described by a technical term and all related jargon expressions, as identified by the algorithm. Subsequently, we monitored all tweets that reported a combination of symptoms satisfying the case definition query. In order to geolocalize messages, we defined 3 localization strategies based on codes associated with each tweet. We found a high correlation coefficient between the trend of our influenza-positive tweets and ILI trends identified by US traditional surveillance systems. |
| first_indexed | 2024-12-10T13:22:17Z |
| format | Article |
| id | doaj.art-b99ddb38682d408bbcaf5c8d9351a2c9 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T13:22:17Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b99ddb38682d408bbcaf5c8d9351a2c92022-12-22T01:47:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01812e8248910.1371/journal.pone.0082489Influenza-like illness surveillance on Twitter through automated learning of naïve language.Francesco GesualdoGiovanni StiloEleonora AgricolaMichaela V GonfiantiniElisabetta PandolfiPaola VelardiAlberto E TozziTwitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using naïve, everyday language. We developed a minimally trained algorithm that exploits the abundance of health-related web pages to identify all jargon expressions related to a specific technical term. We then translated an influenza case definition into a Boolean query, each symptom being described by a technical term and all related jargon expressions, as identified by the algorithm. Subsequently, we monitored all tweets that reported a combination of symptoms satisfying the case definition query. In order to geolocalize messages, we defined 3 localization strategies based on codes associated with each tweet. We found a high correlation coefficient between the trend of our influenza-positive tweets and ILI trends identified by US traditional surveillance systems.http://europepmc.org/articles/PMC3853203?pdf=render |
| spellingShingle | Francesco Gesualdo Giovanni Stilo Eleonora Agricola Michaela V Gonfiantini Elisabetta Pandolfi Paola Velardi Alberto E Tozzi Influenza-like illness surveillance on Twitter through automated learning of naïve language. PLoS ONE |
| title | Influenza-like illness surveillance on Twitter through automated learning of naïve language. |
| title_full | Influenza-like illness surveillance on Twitter through automated learning of naïve language. |
| title_fullStr | Influenza-like illness surveillance on Twitter through automated learning of naïve language. |
| title_full_unstemmed | Influenza-like illness surveillance on Twitter through automated learning of naïve language. |
| title_short | Influenza-like illness surveillance on Twitter through automated learning of naïve language. |
| title_sort | influenza like illness surveillance on twitter through automated learning of naive language |
| url | http://europepmc.org/articles/PMC3853203?pdf=render |
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