If a book, TV show, movie, restaurant, etc. is incredibly popular (I mean, everyone is raving about it), there’s a chance that you might assign it a positive rating too – even if you weren’t that impressed. Call it peer pressure, call it the sway of the crowd, or call it “herding,” but the fact remains: people are influenced by the positive opinions of others. People are also influenced by the negative opinions of others – we see this all the time in real life.
But what about online (I know, that’s real life too don’t yell at me)? Do these same principles apply, let’s say, on a social media site?
New research from MIT, Hebrew University of Jerusalem, and NYU suggest that they do – at least for the positive persuasion.
Researchers systematically altered the “favorability ratings” for over 100,000 comments on “a major news aggregation website.” They can’t say which one, but it’s one that has similar functionality to reddit (up votes and down votes). What they found was that comments that were inflated to have a positive feedback rating kept getting more and more positive votes, upvotes, likes, thumbs up – whatever you want to call them. It was a “snowball” effect that saw the positively-influenced comments receive a 25% higher average rating from other users on the site.
Apparently, when internet users see that other people like something, they’re more inclined to like it too.
But when it comes to negatively-influenced comments (those that have been downvoted into oblivion), the same sort of “herding” didn’t exist.
From an MIT release:
“This herding behavior happens systematically on positive signals of quality and ratings,” says Sinan Aral, an associate professor at the MIT Sloan School of Management, and one of three authors of the study. At the same time, Aral notes, the results “were asymmetric between positive and negative herding.” Comments given negative ratings attracted more negative judgments, but that increase was drowned out by what the researchers call a “correction effect” of additional positive responses.
Basically, negative feedback tended to be corrected by positive feedback – a “rescue” of sorts.
Of course, the implications of such findings could call into question any sort of online system that ranks or prioritizes based on crowdsourced opinions, votes, or reviews.
“Our message is not that we should do away with crowd-based opinion aggregation,” Aral says. “Our point is that you need solid science under the hood trying to understand exactly how these mechanisms work in a broad population, what that means for the diffusion of opinion, and how can we design the systems to be fair, to have less incentives for manipulation and fraud, and be safe in aggregating opinions.”