Hi, I'm Scientific American podcast editor Steve Mursky, 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.

Hashtag 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-fluor 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 researchers used what's called 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

Ploss 1. Computer scientists Svetlana 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 use Twitter and Wikipedia records, and a

project called Google Flu trends, have scanned specifically for flu-related words.

In contrast, Valcova'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.

Epidemiologist Matthew Biggestaff of the US Centers for Disease Control and Prevention

...