A Query Space as a Network Graph

What is a query space? There is no easy definition, though the best introduction to the concept I have found is in SEO Theory’s glossary by Michael Martinez. Simply, a Query Space is a collection of terms and results related to a set of search queries. For example, a dataset of queries containing the the word ‘coffee’ could also contain additional terms like ‘takeaway’, ‘cup’, ‘illy’, as well as synonyms and other related words, depending on context.

Impure Workspace

Impure.com is a fairly new web based tool for data visualisation and analysis. There is a learning curve, but as with Minecraft, it is worth getting through. Impure.com provides a range of controls, operators, visualisation and other tools within its online interface. There is also a range of examples to learn from.

Using Impure.com’s network tools with data from search queries containing ‘coffee’ and impressions, Impure.com required:

…a table with two lists and a NumberList that somehow express the proximity between the pairs of the list you can build a Network with weighted relations.

Weighty Relationships

Network graph with weighted edges

Relationships between words in queries are determined by matched pairs. The thickness of the line in the above graph is generated using the impression data from the table. The pairs are created by listing each word from a query with another until every combination is listed. For example the search query ‘long black coffee’ would become:

Once impression data from the original term is listed next to each combination, the table would have three lists and start to look like this:

Matching impression data to pairs indicates which pair is more significant in the context of search. Pair count data would not be as appropriate, especially if the objective was to identify lucrative terms within a given query space.

Exploring the Network

Local Network Visualiser

There is one more tool for working with network data that needs to be mentioned: the Local Network Visualiser. This visualisation tool lets the user explore related nodes to a set number of levels. In the example above, the selected term is ‘take’, with both ‘away’ and ‘coffee’ one level removed.

The word ‘away’ is not connected with any other terms in this data while ‘coffee’ is strongly connected, and accounts for the majority of the next level of terms. As the data set was created around the term ‘coffee’, this is to be expected.

Final Analysis of the Analysis

Any analysis is only as good as the data available. The examples here are all based on Google Webmaster query information.

This information has a few limitations. It only provides information on queries that the site appears for and the impression data relating to the queries is heavily influenced by the position of the site in the Search Engine Results Pages (SERP).

As limited as the data is, analysis of a query space can show changes over time. When compared to paid search and keyword tool data, the differences between the terms for which the site is visible and the actual query space as seen from another source of data can reveal opportunities for optimisation or the presence of competition.

Free sites and services like Facebook, YouTube, and Google Search still have to pay their developers, provide hosting, repay investors and generate revenue to keep it all going. Traffic, registered users and great PR do not pay the bills by themselves; at some point cash needs to be involved. This is where the interests of those providing the services and those using them diverge.

Free at a price

There will always be a cost to the end user, and if it is not cash it will be something else. Lack of technical support, poor documentation, slow bug fixes, compromised privacy and exposure to advertising are a few ways operating costs are managed and paid for. Some paid services suffer some of these issues as well, but they are not the norm.

When the user pays, there is a clear cost in losing them and therefore higher expectations of service. When the service is free and the costs are paid for by advertisers and investors, creating value for them is important for the business. The advertising model is often the first choice for generating revenue and targeted traffic or impressions, and richer forms of display advertising become more important. When the user pays, creating value for them becomes important to the business.

Facebook appears to be going through this process now. A lot of the recent changes seem to create more value for advertisers than for some segments of their community. With Facebook being such a dominating presence, this is generating a lot of discussion. With all this focus on user control over data and experience, Diaspora could not have begun development at a better time.

Will you pay?

Diaspora as a social media platform will be interesting, and potentially very disruptive to this space. It looks easily accessible for many users, either through Turnkey or individually installed and operated servers. As a distributed network of easily installed and managed ‘Seeds’ across a variety of servers, Diaspora can be compared to WordPress. Based on the offers on Kickstart for funding, it seems that while the software will be free, access to Turnkey servers and technical support will cost money.

Diaspora at the very least will place a dollar value on privacy and control over your social media profiles, and it will ask one other question: Will you pay for access to a social media platform, either through hosting or a Turnkey server?