How to Use Twitter to Predict Sickness.

How to use twitter to predict sickness.Researchers have showed how Twitter can be used to predict how likely it is for a Twitter user to become sick. They have also used Twitter to model how other factors -- social status, exposure to pollution, interpersonal interaction and others -- influence health. "If you want to know, down to the individual level, how many people are sick in a population, you would have to survey the population, which is costly and time-consuming," said Adam Sadilek, postdoctoral researcher at the University of Rochester. "Twitter and the technology we have developed allow us to do this passively, quickly and inexpensively; we can listen in to what people are saying and mine this data to make predictions." Sadilek also explained that many tweets are geo-tagged, which means they carry GPS information that shows exactly where the user was when he or she tweeted. Collating all this information allows the researchers to map out, in space and in time, what people said in their tweets, but also where they were and when they were there. By following thousands of users as they tweet and go about their lives, researchers also could estimate interactions between two users and between users and their environment.

Social media trends can predict tipping points in vaccine scares..

Social media trends can predict tipping points in vaccine scares.Analyzing trends on Twitter and Google can help predict vaccine scares that can lead to disease outbreaks, according to a study from the University of Waterloo. In the study, researchers examined Google searches and geocoded tweets with the help of artificial intelligence and a mathematical model. The resulting data enabled them to analyze public perceptions on the value of getting vaccinated and determine when a population was getting close to a tipping point. In the study, a tipping point represented the point at which vaccine coverage declines dramatically due to spreading fear, which could cause large disease outbreaks due to a loss of population immunity. As part of their study, the researchers collected tweets that mentioned measles-mumps-rubella vaccine and classified their sentiment using artificial intelligence computer programs. They also collected data on measles-related Google searches. Their mathematical theory on vaccine scares predicted what kind of early warning signals they should observe in the data. They found they were able to detect those signals in data from California before the 2014-15 Disneyland, California measles outbreak, which showed early warning signs of a tipping point two years before the outbreak occurred. Their mathematical model also predicted how the Disneyland outbreak helped push California back from the tipping point by making parents more afraid of the disease than the vaccine.