The study Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks was developed by Universidad Carlos III of Madrid (UC3M), Universidad Autónoma of Madrid, the NICTA of Australia, and the American universities Yale and the University of California-San Diego, was to test what is known as the "sensors hypothesis” on the social networks
In the study researcher used the “the friendship paradox” analyzing a sample of data from 40 million users and 15 billion followers in 2009, the researchers were able to show that each user had an average of 25 followers, who in turn had an average of 422 followers. As a result they found out “sensor-friends” that helped to predict social media movemets.
The study published in the journal PLoS ONE, selected a group of users to take some of their followers as the sensor group. What they have found out is that those “sensor-friends” play a more important role than what was previously believed, because they receive information long before the previously chosen users. “We were really surprised. We thought the method would give us a few hours early warning, but instead it gave us several days, and sometimes even weeks or months,” says co-senior author of the authors, James Fowler, professor of medical genetics and political science at the University of California-San Diego (USA). For example, the sensor model predicted the “viral” rise of the hashtag “#Obamacare” as a Twitter trend, detecting it two months before it peaked on Twitter, and three months before it reached the highest number of Google searches with that name.
Simple and effective
In general, this new method turns out to be very simple and effective for monitoring social networks, according to its creators. Data from just 50,000 Twitter is enough to achieve levels of prediction and to know what will “go viral” across the entire Internet. It can be used in real time, about different topics, in different languages and geographical areas, thus allowing for different contexts to be covered: discovering new opinions in a political debate, predicting social movements, obtaining previous knowledge of consumers’ reactions to new products, or analyzing how messages regarding certain illnesses or epidemics are spread in the public health arena.
This system has certain limitations. It cannot predict information associated with a particular event, such as a football match, or daily news or natural disasters is going to spread “virally”, the scientists warn. However, there are other types of news that it is able to predict, such as social movements (the 15M in Madrid) or ideas that have been moving around the web for a while on a small scale and then later reach the general public. "We found that monitoring social media in this manner offers a whole new way of monitoring the global spread of information about all sorts of topics," comments another one of the researchers, Nicholas Christakis, co-director of the Yale Institute for Network Science, USA. This is undoubtedly a new way of predicting the future by analyzing the data that circulates on the social networks.