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Why use a network approach for social media monitoring?

Social media managers or digital teams may be faced with a number of questions such as:

  • How do we increase the visibility of our messages?
  • How can we increase the number of followers, likes and retweets?
  • How do we become top influencers around certain discussions?
  • How can we make some of our messages viral?
  • How do we gain actionable insight?

From a network point-of-view this translates to:

  • How do we build a network reach?
  • What divisions or groups are present when users mention our brand?
  • Who are the most influential people in the discussion?
  • What exactly are they talking about?

A key benefit of social media network maps and reports created with NodeXL is to bypass the need to read thousands of tweets and messages on a range of topics.

NodeXL reports  can be used for measuring and monitoring not only your own, but also your competitors´ performance.

At the highest level, a network approach allows social media managers to recognize that the shape of their crowd is different from the optimal shape, and use network metrics to guide the transition between the current and desired state.

What is the structure of your brand? Is the structure of your network polarized? Or is it a brand cluster? Figure 3 from Smith, Rainie, Shneiderman, & Himelboim, 2014 provides a guide in contrasting patterns within network graphs:


So, what does the structure of your brand look like on social media?

Get in touch for a consultation on how you can better understand the discussion on your brand, identify key performance indicators , and how you too can gain actionable insight. See maps that have been created by the Connected Action Team here.

YouTube video examining a network graph of Suicide Squad (a fantasy film)

See the full Connected Action report here:

No data was captured or analysed by me at any time in the production of the video.

What does @BuzzFeedUk look like on Twitter?

This blog post presents a network graph and analysis produced by the Connected Action Consulting Group related to @BuzzFeedUk.

The graph below represents a network of 10,592 Twitter users whose recent tweets contained “BuzzFeedUK”, or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The tweets in the network were tweeted over the 9-day, 3-hour, 1-minute period from Friday, 22 July 2016 at 09:45 UTC to Sunday, 31 July 2016 at 12:47 UTC.


BuzzFeedUK on Twitter via NodeXL

Click here to access the full Connected Action report.

In interpreting this graph let us examine  Figure 3 from Smith, Rainie, Shneiderman, & Himelboim, 2014 (copied below), which provides a guide in contrasting patterns within network graphs:


Now Looking back at the Buzz Feed network graph we can see that many of the different groups consist of Broadcast networks. Groups 1 to Group 9 consist of fairly large broadcast networks with different users at the center of each group. This is not surprising as one of the aims of Buzz Feed is to create engaging content which is shareable. 

Within the graph. each group contains a different set of most popular keywords which can be seen at the top left hand-side. The words correspond to news articles and could indicate that different groups are sharing and talking about different Buzz Feed articles.

By navigating to the graph gallery version of the report it is possible to locate the most frequently occurring URLs, keywords, domains, hashtags, words, and co-words overall and by group level.

For the purposes of this post let us further examine Group 1. Using the interactive explorer we can zoom into a group to examine the more central users:

zoom in

BuzzFeedUK is in the centre of Group 1

The user account with the label attached to it is the Twitter account of Buzz Feed UK and it is is at the centre of Group 1. The users around the account consist of Buzz Feed UK’s audience.

Other notable highlights within this group are as follows:

Top URLs in Tweet in G1:

  1. [489]19 Photos Of Black British Graduates Guaranteed To Make You Say “YAAASS”
  2. [381] Now The Treasury Has Got A Cat And He’s Called Gladstone
  3. [229] 17 Maps That Will Change The Way You Look At The World Forever
  4. [122] This Gay Cancer Patient Was Told Fertility Treatment Was Only For Straight People
  5. [106] We Found These Qaddafi Henchmen Wanted For Stealing Millions Living In Britain

Top Domains in Tweet in G1:

  1. [3927]
  2. [144]
  3. [15]
  4. [13]
  5. [4]

Top Hashtags in Tweet in G1:

  1. [46] blacklivesmatter
  2. [36] pokemongo
  3. [31] saintetiennedurouvray
  4. [30] london2012
  5. [27] nakedattraction

Top Words in Tweet in G1:

  1. [4357] buzzfeeduk
  2. [742] 19
  3. [560] living
  4. [555] british
  5. [548] black

Top Word Pairs in Tweet in G1:

  1. [541] buzzfeeduk,19
  2. [489] 19,black
  3. [489] black,british
  4. [489] british,graduates
  5. [489] graduates,living

Buzz Feed UK could then compare the results from Group 1 to other groups within the graph.

Another useful metric the graph and the report produces are Top Influencers ranked by the Betweenness Centrality Algorithm. These can be found in the figure below:

top ten

Buzz Feed UK could use this insight to better understand which articles users are engaging with the most, and use this for actionable insight. They could also graph rivals such as Mail Online and compare the interaction Mail Online receives themselves.

They could also locate top influences related to their account and use the smart tweet feature to target them with relevant content. These are users which lie at the edge of networks and are capable of opening up content to new audiences.

Thanks to the Connected Action team for producing this graph. No data was captured or analysed, at any time, in the writing of this blog post.

Visibrain as a Tool for Cyber Security and Intelligence

Visibrain is a powerful media monitoring tool which has access to the Twitter Firehose which means it has complete access to tweets and is not limited in the amount that can be retrieved unlike the Search and Stream APIs. See more about Twitter APIs here which explains why the difference matters.

The problem often faced by those in the security industry is monitoring social media, online press and blogs in a high risk fast moving environment without being overwhelmed by huge quantities of data.

As Twitter is a news sharing and dissemination platform via Twitter using Visibrain it is possible to monitor a number of social media platforms such as Facebook, and YouTube among others as these are often parsed through Twitter. For example, using data derived from Twitter I was able to identify a blogger who was tweeting and blogging pro raw-milk material which contradicted the advice provided by the Foods Standards Agency.

crisis comms

Figure to show an example alert system

Visibrain would allow an organisation to monitor social media for queries specified by an end user, and if these are triggered an almost instant notification would be delivered. For example, if a bank wanted to monitor mentions of a hacking group and the bank using Visibrain this would be possible.

Additionally, if a local or national authority wanted to monitor mentions of a region or city for keywords such as ‘name of city’ + ‘riots’, or any other threat, Visibrain would sent out an alert almost immediately. Major news stories are often reported on Twitter by citizens and/or journalists before reaching the mainstream media.

Moreover, for those wanting reports produced and delivered almost instantly displaying tweet content, actors, URLS, and so forth, it is possible to receive these alerts via email to almost any location in the world. Below is a screenshot displaying how simple it is to create a report, and the wide range of metrics it is possible to monitor.

case report

Setting up a case report

Here is an example of a client who wishes to receive updates on the Chilcot report, especially alerts when the expressions i.e., tweet content begins to mention Tony Blair, when they are away from their desk:


Designed with the cyber security and intelligence services in mind, Visibrain is a robust service with a range of clients such as:


I’d also recommend checking out some of my previous articles on Visibrain, here, here, and here. Interested in finding out more? Or have a specific question, please don’t hesitate to get in touch (@was3210).

What does @Jo_Caulfield look like on Twitter?

I recently tweeted out a network graph based on the Twitter account of Jo Caulfield, a stand-up comedian and comedy writer. It is a very impressive graph for a single Twitter user, and Jo was also taken by the graph, so I thought I’d write a short blog post explaining what it all means.

The network graph, below, represents a network of 871 Twitter users whose recent tweets contained “Jo_Caulfield”, or who were replied to or mentioned in those tweets.The tweets in the network were tweeted over the 9-day, 1-hour, 57-minute period from Friday, 01 July 2016 at 08:01 UTC to Sunday, 10 July 2016 at 09:59 UTC.

Jo_Caulfield Twitter NodeXL SNA Map and Report for Sunday, 10 July 2016 at 10:19 UTC

Network Graph of @Jo_Caulfield

The network graph is made up of several groups of Twitter users, and the groups are determined by the content of tweets. Group 1 (on the left hand side with Jo in the centre) displays the Twitter audience of Jo Caulfield, which is known as a Broadcast Network. This contain an audience of people who are linked only to Caulfield’s account (see Smith, Rainie, Shneiderman, & Himelboim, 2014). In this group the most frequently occurring words include:

  • jo_caulfield
  • pperrin
  • guess
  • now
  • jo
  • people
  • one
  • go
  • anyone
  • see

By navigating to the graph gallery version of the graph and looking for metrics related to this group e.g. “Top URLs in Tweet in G1′ it is possible to examine metrics by group level. Within each graph it is also possible to contrast the different groups, this is particularly useful when the contrast illustrates a divergent view or market segment. For instance, in the graph above we can see that group 2 is a secondary Broadcast Network centred on the Twitter account of @pperrin. Other groups are focused on different topics, and involve fewer users and denser discussions.

In this post I would like to highlight interesting statistics overall in the graph.

 Three most popular URLs consist of:

Three most frequently used hashtags consist of:

Three most frequently occurring co-words consist of:

Three most frequently occurring domains consist of:

Three most mentioned users consist of:

Three top tweeters consist of:

Three most replied to users consist of:

I could delve into many further aspects of the graph, but I’d like to point you to the NodeXL graph gallery which contains a comprehensive overview of the analytics overall, and by group level.

I’d highly recommend carefully examining  Figure 3 from Smith, Rainie, Shneiderman, & Himelboim, 2014 (copied below), which provides a guide in contrasting patterns within network graphs:

Network Metrics Figure 3

Do you have any questions or are you interested in examining your own network graph? Feel free to drop me a message (@was3210). Thanks to the Connected Action team for producing this graph, and thanks to Neil Erskine from Byline Analytics for suggesting this post. No data was captured and/or analysed, at any time, in the production of this blog post.

New post for the LSE Impact Blog

Today, the LSE Impact Blog published an article co-authored with the Head of Digital from the University of Sheffield titled:

Twitter and crisis communication: an overview of tools for handling social media in real time

How a blog post I wrote in 2 hours took me to Split, Croatia (Part 2)

This blog post continues on from part 1 of this 2 part series and looks at my short time in Split, Croatia.

I arrived a day before the conference so I had some time to explore the absolutely  wonderful  city of Split. Just as we were walking around we could see some beautiful views:

amazing views

I was able to work on my workshop by the coast:


I noticed that on display by the coast of Split there were many Olympic medalists:

Gold medalists.png

We headed to De Belly, a beautiful restaurant in the heart of Split and were able to get our hands on some fantastic dessert. I’d highly recommend this restaurant to anyone visiting central Split.


After that, it was back to the hotel to work on my workshop, so that was my first day in Split!

Overall I really enjoyed my time in Croatia, and I hope to visit again soon. The people are very friendly, and unlike some holiday destinations, you are not hassled by locals to purchase anything.




How a blog post I wrote in 2 hours took me to Split, Croatia (Part 1)

On July 7th, 2015 the Information School ran the iFutures conference (I severed on the committee and operated the social media strategy). I met Sergej Lugovic, from Zagreb University Of Applied Sciences, Croatia at this conference.

I had submitted a blog post for the LSE impact blog and was unsure whether it would be published, Sergej assured me that they would like the post. Three days after the conference on July 10th 2015 the article was published. I kept in touch with Sergej, and he had seen how well my blog post had done receiving thousands of hits and shares.

In June 14-18, 2016 as part of the Contemporary Issues in Economy and Technology (CIET) Sergej was able to organise a workshop that I would deliver on Twitter analytics. Below is an image of me and Sergej shortly before the workshop:


The workshop marked the first collaboration, in history, between Zagreb University of Applied Sciences, the University of Split, the Information School, University of Sheffield, and one of the largest food company in the world measured by revenues, and ranked within the top 100 on the Fortune Global 500 in 2014.

All thanks to Sergej’s hard work.

I would also like to thank the hard work of Dr Boze Plazibat in organising the conference. And for providing a tour of the Department of Professional Studies which hosts state of the art facilities.  I was truly impressed by the department. Below is an image of me with Dr Boze Plazibat, CIET 2016 conference organizer:


Split is a beautiful city and as I arrived a day earlier and left and day later I had the pleasure to do some sight-seeing, and speak to locals. This will be covered in part 2 of the blog post.


New article published today on the Data Driven Journalism Resource

Today I published a co-authored article on the Data Driven Journalism, titled The EU Referendum debate on Twitter: Who is winning on Twitter – vote leave or vote remain? you can read it here.


The EU referendum debate on Twitter

For non-UK readers, the EU referendum is to take place on the on Thursday 23rd June 2016 and  the UK will vote either to remain in or leave the European Union. 

There is much buzz around social media and the referendum and I thought I’d delve into some analysis. However, as an academic looking at this critically, and as having published several papers using Twitter data. I have to state that:

  1. Twitter is a highly non-uniform sample of the population. Not everyone in the UK uses the Internet, and of those that do use the Internet only a sample of those use Twitter.
  2. Twitter also allows members of the public to hold more than one Twitter account, so in theory one user could set up several accounts to post about vote leave or vote remain.
  3. There is also the issue of bots, which are Twitter accounts which tweet in large volumes automatically or look to mimic real users.

With such caveats in place let’s look at some recent analysis produced by using different analytic programs.

Using hashtagify  I located the most frequently used hashtags associated with #EUref. I then used the quick trends explorer offered by Visibrain Focus  to compare the frequency of the VoteRemain and VoteLeave hashtags.

Below is a time series graph of the hashtags  VoteRemain and VoteLeave 

vote reman vs vote leave.png

There are over 900 thousand tweets that contain VoteLeave in comparison to VoteRemain within the last 30 days.This suggests that the VoteLeave campaign is more active on Twitter.

Here is a more complicated graph with a number of hashtags compared against each other such as StrongerIn, Brexit, VoteRemain, VoteLeave:

more time series.png

As the graph above demonstrates, Brexit has been used in over three million instances within the previous month. However, many news articles and general media coverage use this term (see G2 in the NodeXL graph below). Therefore, it is difficult to attach the Brexit  hashtag solely to those whom wish to vote to leave the EU.

Now lets take a look at the data using @NodeXL  which can produce network graphs alongside comprehensive reports which are uploaded to the NodeXL graph gallery.

Using data which was already published by NodeXL, I then examined the EURef hashtag which  is impartial as opposed to VoteLeave or VoteRemain:

The network graph below displays 6,259 Twitter users whose recent tweets contained “EURef”, or who were replied to or mentioned in those tweets over the 3-hour, 46-minute period from Friday, 10 June 2016 at 12:58 UTC to Friday, 10 June 2016 at 16:44 UTC.


The network graph is made up of several groups of Twitter users. Notable highlights of the report are that:

Group 1 (top left)  contains the following most frequently used hashtags:
[8009] euref
[1888] brexit
[1122] bftownhall
[859] voteleave
[360] itveuref
[350] voteremain
[342] strongerin
[330] remain
[302] leave
[288] bbcqt

Group 1 contains Hashtags and URLs which point to the vote to remain and leave campaign, this suggests it may be a polarised group (see Smith, Rainie, Shneiderman, & Himelboim, 2014).

In Group 2 is interesting to observe an isolates group which shows that a number of users which are not connected to each other are tweeting using the hashtag. For instance, they may be tweeting media stores. This is one possibility for why the Brexit term is used so frequently.

There are a range of different domains that are being used within this campaign including Facebook, the Guardian, and YouTube , full list below:

The full report NodeXL report including top influencers, top URLs, top domains, hashtags, keywords, word pairs, and replied to can be found within an interactive version of the graph. This was produced by Marc Smith who resides in Belmont, CA, USA.

I’d also like to mention the ongoing work by colleagues:

Any questions? Feel free to drop me a message (@was3210).

Disclaimer: At no time was any personally identifiable data and/or information physically stored and/or analysed by-myself and/or using any of my own equipment. The post draws on the various analyses conducted by others.

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