Zhou, a data analytics expert, built a personality graph of Takahashi based on the reporter’s Twitter messages. The breakdown is based on “psycholinguistics,” or analysis of word choice, and includes 41 traits.
Zhou honed her analysis by crunching data from both Twitter’s and IBM’s own internal social network. Eventually the conclusion drawn from tweets matched those derived from the deeper information available on IBM Connection.
“Computers can derive people’s traits from linguistic footprints,” Zhou told VentureBeat. “That hasn’t been widely applicable before, because where do you get those linguistic footprints? Now, you can do that with social media and digital communications.”
She claims that the 2,500 to 3,000 words from 200 tweets can be used to create a personality evaluation that is accurate to within 10%.
The research is similar to a new study that analysed 15 million Facebook statuses from 75,000 volunteers. Both make correlations between a person’s word choice and activity patterns with that person’s personality.
The strength of both methods is that the complex algorithms drawn from a huge pool of information.
Basically, it’s big data meets psychology.
“Those intrinsic [personality] traits include what motivates you, what you believe, your fundamental needs,” Zhou said. “Thinking about it, it’s very hard to imagine — in a traditional way — how you could learn someone’s intrinsic traits, aside from standard [psychological measurement] tests.”
Takahashi notes that the IBM research combines text analytics, human-computer interaction, psychology, and large-scale data processing to provide insights that could be used in retail, government, media, banking, and even doctors (who could use the information to guide treatment).
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