From: Deep learning for pollen allergy surveillance from twitter in Australia
Class | ID | Description | Example |
---|---|---|---|
Informative | 1 | Personal reporting including: symptoms (a), treatments (b) or both (c). | ‘90% sure we have either developed hayfever. My eyes feel so dry and my nose is running whenever i go outside’ (a) |
 |  |  | ‘my hayfever better watch out today bc ate a spoonful of local honey AND took antihistamines’ (b) |
 |  |  | ‘Kings Cross triggered my hayfever today and now I can’t stop sneezing. I need a zyrtec’ (c) |
 | 2 | General personal reporting. | ‘Hayfever suuucks’ |
Non-Informative | 3 | Public broadcasting including: marketing (a), news (b), warnings (c) etc. | ‘Himalayan Inhaler to help with asthma & hayfever #himalayansalt #onlineshopping’ (a) |
 |  |  | ‘Scientists in Melbourne have made a breakthrough that could change the lives of asthma and hay fever sufferers’ (b) |
 |  |  | ‘Be careful Victorians: thunderstorm asthma warning. Stay safe. #hayfever #health #bom #weather’ (c) |
 | 4 | Unrelated/Ambiguous. | ‘Very annoying. Could be a preservative or maybe hay fever.’ |