Follow Datanami:
May 23, 2017

Digital Psychometrics and its Discontents: Online Behavior for Psychological Analytics

Occasionally we hear about predictive data modeling aimed at gaining psychological insight that at first blush seems – even to tech sophisticates who pooh-pooh AI alarmism – like a primitive version of something unsettling to come, something too penetrating for comfort, something with the potential to compromise our privacy, even our sense of independence.

This week in Miami at the Linux Foundation’s Apache: Big Data conference, a researcher from Cambridge University discussed project work that builds insights into people based on their social media profiles that, combined with data collected from their smart phones, has the ability to know you better than your friends do, better than your siblings, spouse or partner – maybe better than you know yourself. As for the persona you try to present to the outside world, it can pierce that with ease.

This raises the question: If we can do this in 2017, one can only imagine the psychometric implications AI and high performance data analytics hold for consumer advertising and political campaigns, to name two, in future decades.

Sandra Matz

To be sure, a major theme of the presentation from Sandra Matz, computational social scientist / psychometrician at Cambridge, is the potential for good offered by the field of digital psychometrics, which is the assessment of psychological characteristics using our digital footprints, at scale. She also discussed the need for transparency in psychometric project work as the best defense against worst-case urban legends, which could undermine otherwise legitimate and well-intentioned research.

But she also acknowledged the potential for abuse, exploitation and, if nothing else, lowering the bank balances of consumers targeted by more psychologically acute digital messaging. In fact, she said, Facebook ad messaging based on digital psychometrics has already proven its effectiveness with improved conversion rates and higher average revenue-per-sale results.

The writer of this article admits his vulnerability to the charge of psychometric alarmism. In fact the research presented by Matz does not involve particularly complex algorithms or advanced big data analytics techniques. But this, in part, is what’s disconcerting about the research she shared. For now, it amounts to merely scraping data from Facebook accounts, with permission from the Facebook user, and then tabulating our Likes and the words we most frequently use in messages to Friends. Taken together, our Likes and our words are then associated with psychological traits – our degree of extroversion or introversion, our “agreeableness” or lack thereof.

To read the rest of the story, go to