Do I have a ‘Twitter voice’ & what is it like?


Blog photo Twitter voice April 2015 I always think, if I’m going to ask someone to do something, I’d better try to do it myself first. Clinically, this was the norm for me. If I was going to ask someone to eat puree, drink thickened fluid, draw/write with their non-dominant hand (and the list goes on!), then I should be prepared to do the same. So over the years I’ve ‘enjoyed’ plenty of puree meals, drunk a significant amount of thickened fluids to ‘assuage’ my thirst, and ‘refined’ my skill in drawing and writing with my left hand. In doing so, I learnt more about these things and my own personal preferences. It strengthened the concept of how important the role of the person is in decision making and this has helped to further shape my interactions with people when discussing swallowing and communication as well as making decisions when it comes to my own health.

Before asking other people to reflect on their use of Twitter, I therefore really wanted to look at my own Twitter use and think about my experiences of using Twitter (TwitterMind Study 1 will analyse publicly available Tweets of consenting participants and ask them to reflect on their use of Twitter). Over a period of 3 months, I captured two snapshot datasets of all of my Tweets (all Tweets, Retweets, Replies, and Mentions for @LissBEE_CPSP, captured on the 18/02/2015 and 02/04/2015). Using NCapture, NVivo, Excel, and Gephi software, I was able to look at how much I was Tweeting, at which level I was Tweeting at, what types of Tweets I was sending out, and who I was communicating with.

To start off with I looked at the basics:

Basic data graph

Certainly the amount of Tweets that I generated differed over the two snapshots with over double the amount of Tweets sent out in the first time period as compared to the second time period sampled.

Next I wondered who I might be reaching and how my Tweets were seen in Twitter…

Bruns and Moe (2013) propose that there are three distinct structural layers to be found within communications over Twitter. In structural layers of communication on Twitter they outline the three types of Tweets that compose these layers of communication: the Micro, Meso, and Macro levels.

Caroline Bowen (@speech_woman) has also recently written about strategic tweeting and provides some great examples of Tweets found at the Micro, Meso, and Macro levels.

In essence, I think of it like this:

Structural layers Twitter drawing_4

The Micro level’s reach is small – similar to a face-to-face conversation between yourself and a friend that no one is likely to overhear (i.e., communication with one specific Twitter user). By having @user at the start of the Tweet, generally it will only capture the attention of that @user.

Tweet Micro

The Meso level has the potential to be overheard by everyone in the room (i.e., your followers network). By having . (or any other character) before @user at the start of the Tweet, generally it will capture the attention of those who follow you.

Tweet Meso

The Macro level uses the Meso level type of Tweet and adds in #hashtags which has the potential to reach a much wider audience, like using a loudspeaker in a public space might do in real life. By having . (or any other character) before @user at the start of the Tweet and adding #hashtags, a Tweet has the potential to reach a much broader audience.

Tweet Macro

Across the two snapshots of Twitter data, my use of the differing macro/meso/micro layers changed…

Structural layers data

I was using more Tweets targeting the Macro layer in the second time period and less frequently connecting at the Micro level.

Then, I wondered… What sort of stuff I was Tweeting about?

Stephen Dann’s work has led to a Twitter content classification system that allows us to determine what type of information our Tweets are sharing with the Twittersphere. His work identified six broad categories (with 23 specific sub-categories within them) that Tweets can be classified as: Conversational; Status; News; Pass Along; Phatic; and Spam.

Dann defines each of these broad categories, and has further refined them since this publication (2015) however for the purposes of this blog post I examined Tweets according to the 2010 definitions, as follows:

Conversational – ‘Uses an @statement to address another user’

Tweet conversational

Status – ‘An answer to “What are you doing now?”‘

Tweet Status

News – ‘Identifiable news content that is not UGC’ (UGC: user-generated content)

Tweet News

Pass Along – ‘Tweets of endorsement of content’

Tweet Pass along

Phatic – ‘Content independent connected presence’

Tweet phatic

Spam – ‘Tweets generated without user consent’

Looking at the data it was clear that over time, the type of Tweets I sent out also changed…

Classification data

While I certainly like to Pass Along information over the two time periods, there was a noticeable shift between Conversational Tweets and News Tweets.

And then it was time to create pretty pictures:

Using visualisation techniques previsouly applied to Twitter data (outlined by Stuart Palmer 2013), the graphics below show all of the Twitter data captured across the two time periods sampled. The lines represent Twitter communication travelling in a clockwise direction (from sending @user to receiving @user). A thin line indicates limited interaction, whereas a thicker line represents more frequent communication between the sending @user and receiving @user.

@LissBEE_CPSP gephi_2

By viewing the Twitter data this way, it can be seen that there was heavy traffic from @LissBEE_CPSP to a specific @user in the first time period sampled. Although this also seems to be a trend in the second time period sampled, there is communication across a wider network (i.e., between @LissBEE_CPSP and more @users) than in the first time period (note the change in the number of nodes/circles between the graphics).

So what does this all mean?!

Well, it would seem that across these two time periods sampled, my ‘Twitter voice’ changed across all of the markers that I’ve discussed. Given that this was purely across two samples and a limited time, these results may not be truly representative of my ‘Twitter voice’ overall.

Subjectively, I feel that I am interacting with different @users in different ways more now than when I first joined Twitter as an individual and I still feel that I’m learning how to be a part of the Twittersphere. There is so much that I like about Twitter – those @users that I follow don’t have to follow me, and I also don’t have to follow every @user who follows me. I still ‘hear’ what is said. The flow of information, ideas, and opinions is easily accessible. More importantly, I don’t necessarily need to see or know people in real life (IRL) in order to have connection, whether it be socially or professionally.

On reflection, my ‘Twitter voice’ somewhat reflects my ‘everyday communication voice IRL’. Some days, I like to talk. Some days, I like to listen. Other days, I like to do both. I guess this may also be true for my interactions on Twitter. Sometimes, I Lurk. Sometimes, I Tweet. Sometimes I do both! (If you want to read more on the pros and cons of Tweeting and Lurking, check out the storify of the recent #WeSpeechies debate “Lurking is Better than Tweeting”). The variability and complexity of how we communicate with one another is a large part of what drew me to speech-language pathology originally. It’s interesting that regardless of mode (real life versus online communication), my interactions are changing, evolving, and transitioning over time.


Bowen, C (2015) Webwords 51: Taking Twitter for a twirl in the diverse world of rotational curation – March 2015. Journal of Clinical Practice in Speech-Language Pathology, 17(1):51-53. 

Bruns, A & Moe, H (2013). Structural layers of communication on Twitter. In Weller, Katrin, Bruns, Axel, Burgess, Jean, Mahrt, Merja, &Puschmann, Cornelius (Eds.) Twitter and Society. Peter Lang, New York, pp. 15-28.

Dann, S. (2010). Twitter content classification. First Monday, 15(12). doi:10.5210/fm.v15i12.2745

Palmer, S. (2013). Characterisation of the use of Twitter by Australian Universities. Journal of Higher Education Policy and Management, 35(4), 333-344. doi: 10.1080/1360080X.2013.812029


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