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How to Mine Social Media for Business Success

by Knowledge@SMU, 27 December 2010

topics:
Management Technology

tags:
data mining online advertising Social media social networks

You would be hard pressed to find a person aged 15 to 35 who has not heard of social media sites like Facebook, Twitter, MySpace and so forth. Facebook alone counts more than 550 million users as of mid-2010 and the number is still growing. Put into context, these 550 million users, if regarded as a country, would be the third largest on the planet – after China and India, and just ahead of America.

Clearly, the latent potential of social networking in business, government and society requires research. It was therefore apt that a group of academics and industry practitioners gathered to share their knowledge on social network mining at a workshop organised by Singapore Management University's School of Information Systems. There, participants shared their insights into the issues.

Professor Jeffrey Xu Yu of the Chinese University of Hong Kong discussed the applicability of graph theory to address problems of nodes on the internet being reachable from each other. Each of these queries, known as ‘Reachability Query’, tries to use classic shortest distance determination via search trees based on its location on the graph to find a pattern matching over the connected set of nodes in the network modelled as a graph.

In the context of mining the social network on the internet by modelling the connected nodes in it as a graph, the method tries to answer questions such as: which nodes are connected to which other nodes on a social network? How many hops does it take for one node to reach another?

Analysing patterns

Bangalore-based Vineet Chaoji, senior associate scientist at Yahoo! Lab in India, lent an industry perspective. Part of his job is to analyse and find patterns within Yahoo’s own network of users, many of whom already have informal social networks.

Chaoji presented a case study from Yahoo! which examined the use of social influence for targeting advertisements to a small network of friends as a means for getting users to sign up to a paid service of PC-to-phone calls.

The study was a means of getting traction to increase user adoption for a paid premium service, by observing how a network of influences could better target their constituencies of followers. “As you probably know well by now, social media has the potential of making internet-based marketing much more effective than it now is – which is [currently] more like a hit and miss affair,” said Chaoji.

Depending on the click-through rates and views of the relevant advertisement, total viewership is usually in the region of 0.1% to 0.001% – or even lower – of all site visits where there are advertisements. The theory is that people are more likely to click on an ad and respond to it when they know trusted friends are already using or endorsing that service or product.

Using data from Yahoo Messenger, Yahoo! mined the density of connections in a sample of users. Chaoji sought answers from a focused set of questions: Do individuals wield influence over their friends in online social networks? Are highly connected individuals the same as social influencers? How far reaching is the influence in the network? Is it local to its social neighbourhood or is it global? Which targeting methods are suitable?

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