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letra de computational social science: making the links (excerpt) - jim giles

infectious ideas
in some instances, big data have showed that long-standing ideas are wrong. this year, kleinberg and his colleagues used data from the roughly 900 million users of facebook to study contagion in social networks — a process that describes the spread of ideas such as fads, political opinions, new technologies and financial decisions. almost all theories had -ssumed that the process mirrors viral contagion: the chance of a person adopting a new idea increases with the number of believers to which he or she is exposed

kleinberg’s student johan ugander found that there is more to it than that: people’s decision to join facebook varies not with the total number of friends who are already using the site, but with the number of distinct social groups those friends occupy9. in other words, finding that facebook is being used by people from, say, your work, your sports club and your close friends makes more of an impression than finding that friends from only one group use it. the conclusion — that the spread of ideas depends on the variety of people that hold them — could be important for marketing and public-health campaigns

as computational social-science studies have proliferated, so have ideas about practical applications. at the m-ssachusetts inst-tute of technology in cambridge, computer scientist alex pentland’s group uses smartphone apps and wearable recording devices to collect fine-grained data on subjects’ daily movements and communications. by combining the data with surveys of emotional and physical health, the team has learned how to spot the emergence of health problems such as depression10. “we see groups that never call out,” says pentland. “being able to see isolation is really important when it comes to reaching people who need to be reached.” ginger.io, a spin-off company in cambridge, m-ssachusetts, led by pentland’s former student anmol madan, is now developing a smartphone app that notifies health-care providers when it spots a pattern in the data that may indicate a health problem

other companies are exploiting the more than 400 million messages that are sent every day on twitter. several research groups have developed software to -n-lyse the sentiments expressed in tweets to predict real-world outcomes such as box-office revenues for films or election results11. although the accuracy of such predictions is still a matter of debate12, twitter began in august to post a daily political index for the us presidential election based on just such methods (election.twitter.com). at indiana university in bloomington, meanwhile, johan bollen and his colleagues have used similar software to search for correlations between public mood, as expressed on twitter, and stock-market fluctuations13. their results have been powerful enough for derwent capital, a london-based investment firm, to license bollen’s techniques

- jim giles

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