Facebook Crowdsourcing to Improve Accuracy in Local Search

One of the biggest problems in local search is the sparseness and inaccuracy of location data. (Okay, I guess that’s two problems.) Google uses the algorithmic route to determine when there are duplicate listings, but a computer can only do so much. Enter Facebook with their ability to put their 750 million users to work.

Sometimes, you just need a human to tell you the answer. In the case of local, the majority of information about a location is stored in the brains of humans and not stored in yellow page listings, websites, directory services, and other entities that a machine could sort through.

Historically, the local listings services have put up walls to ensure accuracy– for example, Bing’s version. While making it harder to get your listing approved (via long required forms and/or arduous verification) it will provide more complete data but discourages the entry to begin with, further exacerbating the local information problem.

Has Facebook stumbled onto the key to ensure accurate search results by gamifying user-generated content?

About the Guest Author:  Dennis Yu is Chief Executive Officer of BlitzLocal, an agency that builds social media dashboards to measure brand engagement and ROI, specializing in the intersection of Facebook and local advertising.

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