Share with a Tweet
In this, my first blog post on teresachinn.co.uk and as the founder of @WeNurses (Twitter chats for nurses) I want to share this data study with you, but please bear with me as this is rather a long post – I promise to try not to make a habit of it! If you are in a hurry though I have created an infographic of some of this data. I have made some conclusions and recommendations but please feel free to make your own and share them via the comments box.
In order to know where we are going we need to have a starting point – in social media and nursing we really have no firm starting point because all of a sudden the #nursecommunity was there, it almost appeared out of the ether as if it had always been there, (of course it was always there we just merely transplanted it into a digital space) so I have used the birth of nursing twitter chats as a point in time by which we can measure. I have looked at some of the social data, using a social media listening tool, pre nursing twitter chats and up to the 3rd quarter of 2012 . By examining and comparing this data we can create a starting point from which we can develop and progress.
This study set out to explore and compare the social media nursing data (specifically Twitter data) from 2011 and 2012.
The study initially searched Twitter using SM2 for the following key words that were known to be areas of nursing discussion on Twitter:
WeNurses; 6Cs; nurseuk; nurchat; nurseshift; nursingtimes; Nttwitchat; studentnursingtimes; nmcnews; thercn; rcnpublishing; LDNurseChat;
Words like nurse, nursing and nurses were avoided as they have general uses for the public and a basic Twitter search shows that these words are often associated with the advertising of job vacancies and not nursing discussion.
The search looked at the prevalence of tweets containing these key words during two time frames January – March 2011 (pre nursing twitter chats) and Aug – October 2012, the most current data.
Initially the quantity of nursing tweets were examined between the two time frames and from the graph below we can see that volume of nursing tweets has significantly increased – a 536% increase has taken place between the two time frames.
The volume of Tweets along the two time spans was then investigated. The line graph below shows how the volume of nursing Twitter traffic clearly peaks regularly during 2012 Again this shows a dramatic increase however not only is the amount of tweets in 2012 significantly more in number but also it is focussed at the times of organised tweet chats as the majority of the peaks coincide with Twitter Chats.
A search for the top authors of nursing related tweets, those who produce the most content and talk the most, in 2011 revealed that The Nursing Times had the largest share of the voice, with almost no other volume from anybody else:
In comparison the same search for 2012 not only shows that WeNurses have the largest share of the voice but also that others are now creating Twitter content:
If the two graphs above are examined more closely it becomes apparent that the number of tweets have also increased significantly – in 2011 the Nursing Times reaches 438 to be the largest voice whereas in 2012 WeNurses reaches 1942 to be the loudest voice.
We are also able to see who is being talked about the most and again there are differences between 2011 and 2012:
In 2011 (above) it is clear to see that most of the nursing community’s discussion is focussed around the Nursing Times and the RCN, however the 2012 data (below) shows that the nursing community have a broader focus:
The gender of those Tweeting nursing content in the 2012 time period was explored and this had some interesting results, shown below:
It is clear to see that the gender split is 60/40 – where as when we look at the gender split in nursing it is 89.29% female and 10.69% male (NMC Statistical Analysis of the Register 2008) Therefore the Twitter nursing demographic is not a true representation of the actual nursing demographic.
One of the most interesting and significant things that came to light as a result of this study was as a result of the following two charts. SM2 attributes a “popularity” score to each comment it collects (0 being comments with the least social influence and 10 being the most social influence) and it is clear to see that from the 2011 chart that the share of the nursing voice on twitter is attributed across the scale, with the comments varying almost equally from 10 (influencers in social media) to 0 (those with little or no social media following/influence):
However the 2012 data shows that this landscape has altered significantly, with those with the lowest “popularity” index rating having the largest share of the voice:
This shows that it is now the community and not the most powerful people/organisations who are conversing and adding value to each other via Twitter.
The data clearly shows that there has been a significant increase in nursing activity on Twitter since 2011 and not only has the activity increase but it has also become more focussed with Twitter chats creating periods of intense tweeting. It is also apparent that more people are creating more tweets; from comparisons of the 2011 and 2012 data it can be seen that those people creating the tweets has increased and that the focus of their tweets is much broader in 2012.
The gender split of the 2012 data shows that the Twitter demography is vastly different to that of the nursing population not only will future study’s will need to take this into account this should be a key goal for nursing organisations on Twitter. A true demographic representation of nurses on Twitter will allow for a greater validity of social data, crowd sourcing and will give the Twitter community a more realistic online peer group.
The peer index charts show that nursing tweets have shifted from 2011 giving a voice across the board to 2012 showing that those with the lowest peer index scores now have the largest voice. This shows that the nursing community on Twitter is perceived as non hierarchical as those with low peer index scores able to communicate and tweet comfortably and make their voice heard, this is something that we must strive to preserve.
This nursing Twitter data allows some insight into how things have changed over the last 6 months and creates a baseline to work with. From this baseline it can be seen that:
- nursing on Twitter is growing at an exponential rate and that tweets are no long central to one or two accounts but spread across several organisations
- the demography of tweeting nurses is not a true representation
- the community and it is the community that has the loudest voice
Therefore it is recommended that:
- as growth continues that the diversity of organisations central to nurse content does to in order to reflect the diversity of nursing
- that organisations encourage more female nurses to tweet
- that the whole community continue to listen, respond and share with each other regardless of peer index or hierarchy