What if we can predict social movements, consumer reactions or even possible outbreaks of epidemics up to two months in advance just by monitoring 50,000 Twitter accounts?
London: What if we can predict social movements, consumer reactions or even possible outbreaks of epidemics up to two months in advance just by monitoring 50,000 Twitter accounts?
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Possible.
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Researchers have found a simple and effective method for monitoring social networks where data from just 50,000 Twitter accounts is enough to achieve these levels of prediction and to know what will “go viral” across the entire internet.
“If we could do that, we would be able to predict that viral spread, which would allow us to better understand social mobilisation, debates regarding opinions, health, etc., and to determine how they become global,” explained Esteban Moro Egido from Universidad Carlos III of Madrid (UC3M), Spain.
The aim of the research was to test what is known as the "sensors hypothesis” on the social networks.
To achieve this, the scientists made use of one of the properties of the social networks known as “the friendship paradox”.
Your friends have, on average, more friends than you. In the case of Twitter, after 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, that is, almost 20 times as many.
“This means that a person’s followers have a role in a social network that makes them very relevant when it comes to spreading or receiving information,” explained Manuel García Herranz from Universidad Autanoma of Madrid.
They randomly selected a group of users and take some of their followers as the sensor group.
They found that those “sensor-friends” play a more important role than what was previously believed, because they receive information long before the previously chosen users.
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.
“It can be used in real time, about different topics, in different languages and geographical areas, thus allowing for different contexts to be covered,” the study, published in the journal PLoS ONE, noted.
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