用社交媒体文章预测流感

Hi, I'm Scientific American podcast editor Steve Mirsky. And here's a short piece from the April issue of the magazine, in the section we call Advances: Dispatches from the Frontiers of Science, Technology and Medicine:

#Flu by Rachel Berkowitz Forecasting influenza outbreaks before they strike could help officials take early action to reduce related deaths,

which total 290,000 to 650,000 worldwide every year. In a recent study, researchers say they have accurately predicted outbreaks up to two weeks in advance-using only the content of social media conversations.

The findings could theoretically be used to direct resources to areas that will need them most. A team at the Pacific Northwest National Laboratory in Washington State gathered linguistic cues from Twitter conversations

about seemingly non-flu-related topics such as the weather or coffee. Based on this information, the researchers nailed down when and where the next flu outbreaks were likely to occur.

The investigators used a deep learning computer model that mimics the layers of neurons and memory capabilities of the human brain. Their algorithm analyzed how Twitter language style, opinions and communication behaviors changed in a given period

and how such changes related to later reports of flu outbreaks. The study was published in the journal PLOS ONE.

Computer scientist Svitlana Volkova, who led the study said the beauty of the deep-learning model we use is that it considers emotions and linguistic clues over time to predict the future.

Previous efforts to forecast flu outbreaks via the Internet- including studies that used Twitter and Wikipedia records and a project called Google Flu Trends-have scanned specifically for flu-related words.

In contrast, Volkova's work examined 171 million general tweets and outperformed other models that were based exclusively on word searches or clinical data suggesting an imminent outbreak.