In my last two posts I introduced the Econsultancy Twitter network, and wrote about how we could use social network analysis to identify influencers and innovators in this community.

In this post I'll look at how mapping a network can help us identify sub-groups in the community and target content to them more effectively.

Detecting sub-groups in the Econsultancy Twitter network

The most famous example of a sharply divided online community is Lada Adamic's analysis of US political bloggers during the 2004 presidential election.

Her visualisation clearly shows Democrat (blue) and Republican (red) camps, with very little communication between them.

Lada Adamic's famous visual of Democrat and Republican blogs during the 2004 US election
Lada Adamic's famous visual of Democrat and Republican blogs during the 2004 US election (source: Lada Adamic)

Online communites are rarely this sharply divided, especially when they revolve around (hopefully) less contentious topics such as digital marketing.

In these networks, sub-groups overlap and don't have clear borders. A person can also be a member of several groups at the same time.

Nevertheless, most social network analysis software allows us to identify sub-groups or cliques within the community, and this is a useful way to probe any community and work out how it divides up between related interests and concerns.

Network visualisations also encode attributes as position, so users close together in the visualisation tend to be closer together in outlook, expertise and attitude (because they are connected directly and/or share mutual connections).

The top 300 influencers in the Econsultancy Twitter network, coloured by sub-groups identified by network software
The top 300 influencers in the Econsultancy Twitter network, coloured by sub-groups identified by network software

By examining the Econsultancy network we find areas of the network dominated by digital marketing specialisms (eg SEO, social media marketing) or tweeters from a specific region (such as London, Asia, or the East Coast of the US).

As marketers, it's also useful for us to know that users in the centre of any network – the 'network core' – tend to embody norms and have more power over the flow of information.

There's a reason we refer to 'fringe' beliefs, and that revolutions and social upheavals tend to happen when marginalised people become connected – think of the Arab spring, or even the London riots.

In the Econsultancy network, the @Econsultancy account itself is very central, as are those of Econsultancy employees such as @gcharlton and @lakey.

The full Econsultancy Twitter network segmented into communities
The full Econsultancy Twitter network segmented into communities. Users close to the centre of the network are able to control the flow of information and are more likely to embody community norms.

Using network insights to develop content

In offline networks, networks of relationships within companies and other organisations for example, influence tends to map loosely (but not completely) onto rank and positions of authority. Online and in social media, however, influence is much more about platform-specific qualities, such as the quality of the content you share.

For this reason, looking at online influencers gives us important clues about the type of content we should create. The content an influencer has shared is often what has made them influential, giving them the reach into the community we aspire to.

By segmenting influencers by sub-group, we can also develop a content strategy that reaches all parts of the network with equal effectiveness.

For example, we may have planned to create content that would go down a storm with a certain group in the network, but leave others cold. We might be able to tweak it to appeal to all members of the network, or have different content strategies for different sub-groups.

Pulling it all together

So if you had a client who was interested in reaching members of the Econsultancy network on Twitter, how could you use this information we'd found?

Firstly, you should look to develop relationships with the people we've identified as having high centrality or betweenness. These are the people with the most reach into the network, the most visibility to others, and the most power to make and break messages.

Next, you should look to make sure you're working with a set of influencers who are able to reach the whole of the network. It's pointless working with a group of highly visible people if they are all highly visible to the same small group. We need to make sure we have a good spread and can reach all the people who matter.

Finally, you should optimise your content. The influencers and sub-groups we've identified give us clues about what our community finds interesting and what kinds of content they are looking for.

The potential of social network analsysis

In this series of posts I've only touched the surface of the insights that are available through network analysis. SNA has lots more potential – for example, it can help us understand how information flows through our audience, how many people we need to persuade to reach a critical mass of advocacy, or how we can increase the chances our content goes viral. 

But I hope I've given you a sense of the enormous potential this technique has for our work as digital marketers. 

Please let me know what you think in the comments!

Andrew Lamb

Published 7 November, 2013 by Andrew Lamb

Andrew Lamb is Senior Digital Strategist - Healthcare at Ketchum / Inspired Science and a contributor to Econsultancy. You can connect via Twitter or  Google Plus

3 more posts from this author

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Comments (2)



It's good that this article is encouraging people to think about how they can use social network information much more strategically. It's also important to measure the outcomes from applying this knowledge and feed that back into the plan.

almost 5 years ago


Hugh Anderson, Co-Founder & Director at Forth Metrics Limited

Clever stuff, Andrew. Thanks for the short series. I've enjoyed it and am interested in the potential development via the application of this science.

One of the challenges is to make this stuff simple and consumable by marketing professionals for whom the science of SNA is simply too complex.

As such, I'd be interested to learn more about the tools you are using to state "most social network analysis software allows us to identify sub-groups or cliques within the community" and "The top 300 influencers in the Econsultancy Twitter network, coloured by sub-groups identified by network software". Can you elaborate?

(If you'd rather do that in private, please do contact me offline.)

almost 5 years ago

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