Playground to Prediction: Misinformation and Social Analytics

When I was a kid, there was a hugely popular commercial for Life cereal featuring a kid called Mikey. One day, a strange and horrible tale started to make its way around the playground.

“You know that kid Mikey? From the Life cereal commercial? I heard he ate Pop Rocks and drank Coke at the same time, and his head exploded.” “No way!” shouted another kid. “Way!” retorted another. And so on.

In those days, there was no Internet except the Internet of conversations shared on playgrounds, at slumber parties, in offices, classrooms, bars, busses and anywhere else people tended to congregate. Eventually, it moved to email, then to the Internet, where has been settling countless bar bets since 1995.

But with the social web, urban legends have the ability to morph instantly into memes, and flit around the globe in seconds. When you consider that social web traffic is highly “news” driven and that re-tweets can actually create a news cycle where one previously didn’t exist, you have the potential for flash-points of misinformation, which lead to confirmation bias, which essentially means that people have a tendency to believe things they’ve already heard or think to be true. The more you hear it, the more “true” it becomes.

(If you doubt this, ask Gene Simmons, Johnny Depp, George Clooney, Miley Cyrus or any of the other celebrities whose deaths are routinely–and prematurely–announced on social networks. Conversely, witness the extreme care taken by news outlets to seek official confirmation of the death of Steve Jobs, which had previously and erroneously circulated many times before.)

But misinformation isn’t confined to childish rumors and celebrity death-hoaxes; consider its power to topple political candidates, financial markets–even businesses and governments, when used with specific, malicious intent.

Craig Silverman of the Columbia Journalism Review recently posted an excellent article on the subject of misinformation in which he interviewed Professor Filippo Menczer of Indiana University on the work he is doing trying to understand the propagation of information, true and false, on the Internet.

While the article focuses primarily on the impact to consumers and the media industry, it’s a critical read for business as we start to envision how to realize the promise of predictive analytics. Because confirmation bias is so strong on the web and has such potentially enormous downstream consequences, Silverman and Menczer argue, “early detection is a must-have for any misinformation detection system.”

Think of it as an “information supply chain;” fallacies early in the chain can have disastrous consequences later on as they propagate and influence the decision-making process. This is even more critical as businesses become flatter, more networked, more real-time and more collaborative.

Given the complexities of human communication and the algorithmic acceleration of information on the web, Menczer’s research should be hotly anticipated by anyone planning to incorporate social data into decision support. To realize the dream of predictive analytics, we must have unshakable trust; if not in our data, then at least in our ability to interpret it.

About susanetlinger

Industry Analyst at Altimeter Group
This entry was posted in behavior, Listening, Predictive Analytics, Research, Social media, Social media measurement, Uncategorized. Bookmark the permalink.

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