For several years now there has been talk of Google using social signals alongside the familiar metrics of search engine optimization in establishing page rankings; in fact, it already does so to an extent. Twitter retweets, for example, factor into its organization of news stories as it establishes which are most relevant.
Social signals come in many forms. They could be retweets or favorites on Twitter, likes or shares on Facebook, favorites on YouTube, or +1s on Google+. Each of these signs carries subtly different social weight and will need to be weighted accordingly in any new ranking algorithm.
Social weighting has already been seen in operation on a smaller scale in some search systems that take into account the histories of the person searching and that person’s friends, but applying it in a universal rather than personal ways requires a whole new level of organization. It is probable that it will develop in stages and be subject to a number of tweaks before it starts working as effectively as link-based algorithms work now.
Not quite. There is a subtle difference in that only a limited section of the internet population can create links – those with websites. Creating a website requires money – only a small amount in most cases, but enough to be a barrier in many parts of the world – and a degree of administrative, if not technical, skill. This means that only a limited demographic participates in it. A ranking system based only on links therefore reflects the views of only a subsection of internet users and not the whole mass of users. In business terms, this means it is impossible to know what some customers want.
Social media is different. Almost anybody with internet access can easily access some kind of social media and send social signals through it. This means that ranking systems can use it to take into account the views of a wider selection of users. It can also take into account casual, impulsive declarations of interest that don’t last long enough for the creation of links but are still relevant in assessing which sites might hold people’s attention long enough for them to deserve a high ranking. It is a system that better reflects the interests of varied users and of the overall mass of prospective customers.