Facebook Social Search Engine

I’ve been a long time campaigner to get Facebook to fix their internal search engine, and it now finally looks like they’ve listened! Thanks in the most part to 2 ex-Googlers Facebook are to start rolling out what’s being called “Graph Search” (with more than a hat tip to the ‘Open Graph‘ protocol). Previous results were always primarily contextual to your own preferences, returning friends and pages where they matched your query, and backed up by Bing web search results when unmatched. The result of the upgrade is still constrained to the Facebook world – it’s walled garden – but there are apparently noises about expanding to include the wider web – nirvana. Utilising the following facets – People, Photos, Places, and Interests – people can ‘vertical search’ their friends for advice. So an example would be searching a location for recommendations for something to do e.g. “mexican restaurant recommendations in San Francisco”. Not having experienced this there is no doubt that these fantastic new results listings will not only change how we use the output but also how we consider our inputs, and also who we connect with in a wider context. Good for us – no doubt; good for Facebook – inevitably; good for advertisers and big data collectors – certainly. But most importantly for the social web it gives those of us who’ve been on Facebook for a few years now some reason to want to keep using it rather than losing it thanks to it’s fading patina. Here’s Chango’s Dax Hamman’s thoughts on monetisation: http://www.huffingtonpost.ca/dax-hamman/monetizing-facebook-graph-search_b_2504959.html Here’s John Batelle (author of The Search) on his take on Facebook Graph Search: http://battellemedia.com/archives/2013/01/facebook-is-no-longer-flat.php  ...

Facebook Edgerank Algorithm Explained

Facebook’s Edgerank is probably now the second most used search algorithm. Google’s (natural) search engine  algorithm is the first… Bing & Yahoo used to have a look in, and until recently it’s been Apple and Amazon but thanks to Facebook’s ‘always-on‘ nature the Edgerank algorithm is hard at work in the background. Technically it’s not a search algorithm – it’s more like a measurement tool for reach and impact. But understanding what it is and how it works no doubt helps you understand how to lay out your stall better to ensure your marketing is maximised within the Facebook marketplace. Simply put the algorithm determines what appears in Facebooks users’ news feeds. Here’s the basic principle: Facebook looks at whether or not you’ve previously interacted with an author’s posts or whether or not your friends are engaging around those posts If content is or isn’t engaged by your friends and the network at large, affects what you see and what you don’t see EdgeRank also examines whether or not your have interacted with similar types of posts in the past, i.e. photos, videos, polls, etc. If content or page hosts have received complaints by other users, chances are that you will not see it. This is all stacked up against time – engagement very quickly drops off after an update post. As Mark Lock on Business Insider noted in 2011 the half life of the average Facebook update post is 1hr 20mins! The algorithm itself looks like the below, but here is a brief definition from Techcrunch: …every item that shows up in your News Feed is considered an Object. If you have an Object in the News Feed (say, a status update), whenever another user interacts with that Object they’re creating what Facebook calls an Edge, which includes actions like tags and comments. Affinity Affinity is a score based on the proximity or how “friendly” you are with someone. You’ve probably seen this in action. Comment on someone’s photos and you’ll find them appearing in your feed more often. As Kelvin Newman points out in his definitive Econsultancy article “…affinity is one-way. This means you visiting a forgotten friends profile doesn’t increase the likelihood of you appearing in their newsfeed.” So you can’t dupe the algorithm this way! Edge Weight This is a formula which decides which pieces of content are more likely to appear in news feeds than others....