Crowdsourcing doesn’t work without critical mass. A business review on Yelp with 1,000 individual reviewers is statistically much more credible than those with sparse number of reviews. Now that Facebook “Likes” seem ubiquitous, Facebook plans on harnessing the Likes that map their Open Graph to develop new methods for social search based on what the crowd likes.
AllFacebook (an independent resource not associated with Facebook) is analyzing how the Facebook “Like” functions like a link:
While there was a lot of speculation about Facebook’s search strategy, the company has confirmed with us that “all Open Graph-enabled web pages will show up in search when a user likes them”…
Under this system “link baiting” will give rise to “like baiting”, which is how Facebook determines the relevance of information. This has become a full scale attack on Google on all fronts at this point as Facebook has officially entered the internet search market. While many of the details of the Open Graph were initially revealed at f8, it wasn’t clear what Facebook’s complete strategy would be and how big of a threat this would be to Google.
While we suggested that the like had just replaced the link, it has now become abundantly clear what Facebook’s intentions are. Facebook wants to launch the social semantic search engine as we alluded to during f8. Now that the search results are officially showing up as Facebook search results, the war has begun.
With Facebook building the semantic search engine based on data mining “likes”, the semantic web starts evolving in a surprising direction. Instead of building on top of the tagging and parsing of traditional media content, the semantic search engine can be built on the mass aggregation of the efficient one-click “like”. Even further down this road towards simplicity, the simple 140-character tweet will also attain semantic power when Twitter facilitates the inclusion of Twitter annotations to every tweet.
Twitter posts already contain plenty of metadata that allows for smart filtering and organization, including date and location. With annotations, however, the metadata possibilities will be literally endless. Tweet metadata could eventually contain information or links based on words or phrases in the tweet itself, other options added to the tweet, or even other external data like the weather in the senders location at the time it was sent. Imagine being able to add an infinite number of hashtags to a post without wasting precious characters.
The data mining of Facebook’s Open Graph and the metadata of Twitter tweets will give the simple acts of liking and tweeting more power to influence as new search engine criteria. Practically speaking, it will spawn new methodologies for getting recognized by social search. The most obvious one is simply just to get “liked”.