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:

Figure-3

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.

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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

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:

Figure-3

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] buzzfeed.com
  2. [144] twitter.com
  3. [15] linkis.com
  4. [13] middleeasteye.net
  5. [4] co.uk

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:

alert

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

clients.png

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.

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.

nodexl

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:
[798] twitter.com
[685] co.uk
[449] twimg.com
[183] facebook.com
[129] trib.al
[98] org.uk
[97] theguardian.com
[88] youtube.com
[88] ac.uk
[54] buzzfeed.com

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.

What does @paulmasonnews look like on Twitter?

Grateful for a recent favor from Paul Mason, (writer, broadcaster, film-maker, and recent author of Postcapitalism: A Guide to Our Future) I thought I’d report findings on his Twitter network graph. Thanks goes to my Connected Action Partner Marc Smith for generating the graph.

The graph below is of 2,416 tweets that contained ‘paulmasonnews’ taken from a 9 day period starting Tue May 24th and ending Thurs 02 June, 2016 (the graph gallery version of the graph can be found here)
paulmasonnews Twitter NodeXL SNA Map and Report for Thursday, 02 June 2016 at 18:06 UTC

The network graph is made up of several groups of Twitter users. Group 1 displays the Twitter audience of Paul Mason, which is known as a Broadcast Network which contain an audience of people who are linked only to Mason’s account (see Smith, Rainie, Shneiderman, & Himelboim, 2014).

I’m always interested in seeing the most frequently shared URLs in a graph and the top 5 most shared related to Mason consist of:

  1. Spanish article by Juan Carlos Monedero which analyses Mason’s new book Postcapitalism
  2. Mason’s own article for the Guardian on How to make James Bond relevant – make him battle Trump and the oligarchs
  3. A tweet by Mason related to Corbyn and Labour
  4. A link to a spanish live-stream titled Ciudades Democráticas (Democratic cities)
  5. A tweet by Mason on  Uberisation and Europe

The report shows there is much discussion surrounding Mason’s recently released book Postcapitalism: A Guide to Our Future.

The following are notable highlights:

  • The most frequently shared URL is based on this book.
  • Moreover, the keyword postcapitalism appears in the URLs of G2, G3, G7,
  • Postcapitalism it is the third most frequently used hashtag overall and the hashtag makes an appearance in G1, G2, G3, G6, G8
  • it appears in G6, G8 as among the top keywords
  • In G8 the top word pairs include:
    • [29] postcapitalism,paulmasonnews
      [28] penguinukbooks,paperback
      [28] paperback,postcapitalism
      [28] paulmasonnews,published
      [28] published,today

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.

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.

Tips for a Professional Social Media Profile

I’m always asked for tips and tricks form academics or those from industry starting out on Twitter and other social media platforms. So I thought I’d collate my responses into 5 points.

I draw on principles covered in the Art of Social Media: Power Tips for Power Users which is a really good read for anyone thinking of building a profile on social media. In no particular order here are 5 tips:

  • Pick a relevant screen name and/or Twitter user handle. I would suggest using your name and including it as your ‘@’ handle e.g., @JohnSmith.
  • The overall look of your profile is very important as the users visiting will make a snap judgement with the information you provide, and the image that you portray. I’d say scrap the long paragraphs and communicate key points.
  • You need a good profile picture, it has to look professional, and it has to be of your face. Stay away from poor quality pictures and if necessary hire a professional to take a picture. Make the picture the brand and use the same picture across social media platforms.
  • If possible, have a blog and/or website unique to yourself that you list across your social media platforms. Produce content relevant to your discipline that can be shared and linked back to you.Examine what is in the media and see if you can draw a link from the work in your discipline either for a tweet or a blog post.
  • Follow users within your discipline, and remember to engage with other Twitter users rather than solely post your own content. If you like, retweet, or share another users post then they are likely to reciprocate.
  • Produce regular content and produce a content sharing plan. Alternatively, you can use a social media managing platform such as Hootsuite or Buffer. When considering what to share remember that visual posts gain more engagement, and tweets with hashtags will attract a larger number of views and impressions. The time of sharing a post will affect how many impressions and engagements it will gain, so don’t be afraid of sharing similar content throughout the day. Below is a useful table adapted from the Art of Social Media: Power Tips for Power Users:

Table 1 – Recommended number of posts per social media platform

Platform
Casual users
  Hard core users
Facebook
1-2
3-4
LinkedIn
1
4
Twitter
8-12
25

These tricks will certainly increase engagement on your social media accounts.

One of my clients Gary Spence, Managing Director of Hybrid Supply Chain Ltd, was able to increase his social media statistics significantly within a matter of weeks by following this methodology:

Figure 1 – Increases in Gary’s social media statistics within 1 month

increases

Do you want help running a professional social media account? Drop me a message for a quote!

References 

Art of Social Media: Power Tips for Power Users

Acknowledgments

Many thanks to Andrew Latchford, from the VP Group Ltd, for inspiring this post.