title: Influenza-like illness surveillance on Twitter through automated learning of naïve language.
abstract: 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.
authors: Multifactorial Diseases and Complex Phenotypes Research Area, Bambino Gesù Children's Hospital IRCCS, Rome, Italy.
authors: Francesco
authors: F
authors: Gesualdo
authors: Giovanni
authors: G
authors: Stilo
authors: Eleonora
authors: E
authors: Agricola
authors: Michaela V
authors: MV
authors: Gonfiantini
authors: Elisabetta
authors: E
authors: Pandolfi
authors: Paola
authors: P
authors: Velardi
authors: Alberto E
authors: AE
authors: Tozzi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D020407:Internet; D011159:Population Surveillance / Q000379:methods*; D009626:Terminology as Topic