By Mark Redgrave 

The growth of social media over recent years has been truly exponential.  Indeed, Wikipedia, YouTube, Blogger, MySpace, Twitter, and WordPress are all firmly established in Alexa’s 20 most trafficked websites.   As was succinctly put by Oxford lecturer Dr Kate Blackmon at this week’s Silicon Valley Comes to Oxford event, the future was not about crowd sourcing but crowd filtering.  There are now enough social networks to fill all the obvious niches but making use of the stream of information that pours into them is something we've only just started.

With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations. As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of semantics.  “If Web 2.0 was all about democratising publishing, then the next stage of the Web may well be based on democratising data mining of all that content that's getting published,” commented Marshall Kirkpatrick at ReadWriteWeb.

Arguably one of the most interesting developments this year was the realisation amongst the online advertising community that semantic technology will play a pivotal role in monetising social media. 

Keyword targeting forms the cornerstone of online engagement, user targeting, and ad serving for the majority of the digital media community but it is fundamentally unintelligent and inaccurate. The problem is that most sentiment analysis algorithms rely on us using simple terms to express our sentiment about a product or service. However, cultural factors, linguistic nuances and differing contexts make it extremely difficult to turn a string of written text into a simple pro or con sentiment. The fact that humans often disagree on the sentiment of text illustrates how big a task it is for computers to get this right. 

Furthermore, although many companies now understand the value of sentiment data, and there is a strong desire within the ad industry to harness advances in semantic technology, many are restricted by the limitations of their keyword based targeting systems.

With considerable investment now going into semantic research and development, we are very close to seeing it being delivered in a form that can be quickly and easily harnessed by the online advertising community.  If 2009 was the year that semantic technology was identified as the key to monetising social media, then 2010 will be the year that it is adopted.  The implications of this are vast – it will not only dramatically improve ad targeting and address brand safety issues, it will create an entirely new playing field for the digital marketing industry.