Science for SEO

The paper we look at here is called "Mining Social Networks Using Heat Diffusion Processes for Marketing Candidates Selection" and is by Yang, Liu and King from The Chinese University of Hong kong.

Companies have started social networks more and more for WOM promotion, increase bran awareness, attract potential clients and so on.  This is openly apparent in Facebook pages, Twitter, collaborative filtering, blogs and many many others.  This paper presents a model enabling marketers to make the best use of these networks using the "Heat Diffusion Process" (which is an idea borrow from the field of physics).  They have 3 models and 3 algorithms to demonstrate that allow for marketing samples to be collected.

In physics the heat diffusion model states that heat flows from a position of high temperature to one of low temperature.  

They show that these methods allow us to select the best marketing candidates using the clustering properties of social networks, the planning of a marketing strategy sequentially in time, and they construct a model that can diffuse negative and positive comments about products and brands to simulate the complex discussions within social networks.  They want to use their work to help marketeers and companies to defend themselves against negative comments.  The idea is to get a subset of individuals to adopt a new product or service based on a potential network of customers. 

The heat diffusion model has a time dependant property which means that it can simulate product adoptions step by step.  The selection algorithms can represent the clustering coefficient of real social networks.  All users of social networks can diffuse comments that can influence other users. Based on this they say that nodes 1 and 2 represent adopters and the heat reaches nodes 3,4 and 5 as time elapses.  The users in the trust circle of other users have a greater influence.  Not all of the people in the trust circle however will be contacted about the heat source.  Also some users are more active than others in diffusing information.  they observe that bad news or negative comments diffuse much faster than other news.  

Individuals are selected as seed for heat diffusion.  The influence of individuals is based on the number of individuals they influence. The heat vector they construct decides on the amount of heat needed for each source.  In order for them to diffuse properly they need a lot of heat.  Thermal conductivity is then calculated.  It sets the heat diffusion rate.  The adoption threshold is then set, as if one consumers heat value is higher than others, they are likely to adopt this product.

If a user doesn't like the product, s/he is allocated negative heat as they will diffuse negative comments.  At some point someone in the network will provide different information which might be positive.  If that user adopts the product anyway, they will diffuse positive comments.  Two defense candidates are then selected and then the negative impact is alleviated.  

"So far, our work considers social network as a static network only, and ignores newcomers, new relationships between existing members and the growth of the network’s size. In the future, we plan to consider the evolution property of social networks, and permit our social network to grow at a certain rate"   

This paper shows a new and very different way of analyzing social networks.  It gives you a nice opening ti discussions concerning this in a different light.  The current methods used in business are not foolproof and such research shows us how simplistic they are and how they can actually be misleading.

There are some issues with these systems which cause degradation in user feedback, here are a few:

There is a lot of research going on to resolve these issues, take a look at these to start with:

Complexity and Social Networks Blog of the Institute for Quantitative Social Science and the Program on Networked Governance, Harvard University 

"A Few Bad Votes Too Many?  Towards Robust Ranking in Social Media" (Jiang Bian, Yandong Liu, Eugene Agichtein, Hongyuan Zha)

"Dynamics of Collaborative Document Rating Systems" (Kristina Lerman)

I think the isse at the moment isn't selling products via SN but rather going in unintelligently with the hard sell, and the spamming.  The way to sell your products and services is to find interested individuals and to approach them in a friendly, social networking way, about your stuff.  Use social networking etiquette.

Although Sphinn is small in comparison to Digg, it uses the same kind of system.  People submit stories and they get voted by other members.  The posts with the most votes go to the "Hot topics" page, which is also the content that you'll get in your feed.  Basically, it's the same thing.  People post advertising for their products and services instead of information rich resources that can be shared with the community.  It's also a drain on resources.  All in all, a nasty thing that needs to be dealt with.

Ways that this problem can be solved include:

Most people will be aware of this, it's basically ranking results by relevance, but first you have to decide what's relevant.  

On Sphinn, new submissions come in on the "what's new" section as they are submitted, which I like.  Sometimes stuff I'm interested in doesn't get many votes and they'd be buried before I could come across them (not "find" - I'm never looking on Sphinn, I'm browsing).  This section is easily spammed and to be honest it's not as bad as I've seen it elsewhere.  

There has to be a filter as stories come in to minimise spam at this level.  One way to do it would be to use a topic detection algorithm and train it on a clean already existing Sphinn corpus.  The system can draw patterns from the training data which help it label a submission with "Sphinn" or "Foe".   The patterns will be numerous!  A cool by-product is a way to visualize the community.  

This type of method needs to be flexible as well though, otherwise if you used an unconventional title for example, or weird words, your submission would be chucked out.  The more you train it the better it gets and I would define Sphinn as a closed environment, which makes the problem easier to deal with.  There are only so many categories.  It's not as difficult as tracking spam in a global engine.  On top of that you could take into consideration user interaction to solidify your method.

Or failing all of that, we could beg: "Spammers, please please stop  peeing in the beer".

It uses network analysis which assumes that the way that people communicate affects important properties of that group.  Here nodes are people and edges are communication acts.  It also uses sequential analysis which identifies the constants and rules that govern the inner structure.  

It was developed by Marius I. Benta, a PhD student at University College Cork (Ireland). 

It's a free download (windows, Mac, Linux), and you can see more  screenshots here.

PhD comic suggests:

They're developing a toolkit to support expertise sharing via social networks.  The toolkit "SISN" (Seeking Information via Social Networks) is "is a general purpose toolkit for social network-based information sharing applications that combines techniques in information retrieval, social network, and peer-to-peer system".

Their toolkit requires: