Collaboration through Technology in Teaching and Research

This paper is for NCA 2010. It presents solutions for collaboration in the public relations campaigns course and in research.



Online indentity management & social groups

I came across this presentation on John Bells’ blog (John Bell heads the Digital Influence Team at Ogilvy PR) and had to share it here.

This happens to be one of my research interests, something I alluded to in an earlier blog post, and I am now working to get ready for publication.

The presentation is from Paul Adams, senior UX researcher at Google. I love the connection he makes between social science and social interface/product design. I love the fact that this kind of research happens in a corporate setting, and if I didn’t love teaching so much I’d be jealous of his job.

New Pew report on social media use

Research Study: @sockington is more influential than @chrisbrogan

This Webecology research report has been making the rounds on Twitter. I haven’t had time to read it until now, here are my reading notes:

The Webecology team uses large scale data mining to identify patterns indicative of online culture and community. Wish I’d do this, too – and will, as soon as I find a research partner to help with the data mining part.

For this project, the authors set out to create a more accurate measure of influence on Twitter that goes beyond either:

  1. number of followers; or
  2. followers/friends ratio

The authors defined influence on Twitter as:

influence on Twitter = the potential of an action of a user to initiate a further action by another user

Specifically, influence means the potential of a tweet to generate replies, mentions (conversational behaviors), RTs, and attributions (content-pushing behaviors).

This is an atheoretical, operational definition of influence (the study’s Achille’s heel).

As far as I understand, all 4 actions were weighed equally. So, a RT factors the same as an @reply in determining influence.

They selected 12 Twitter accounts to study. The selection was based on this criterion: the 12 accounts were  “widely perceived to be among the more influential users on Twitter.” It is not clear who did the perceiving, and what definition or measure of influence they used in the process of perception. IMO, the arbitrary selection of the sample is another major weakness – but in this case, I can live with it, because the purpose is not to derive conclusions about Twitter culture as much as it is to demonstrate how the methodology can be used.

Then, the 12 users were grouped into 3 categories. Here is a table with the accounts they analyzed, and their number of tweets over 10 days, as well as the number of followers and friends at the end of the 10 days:

Celebrities Username Tweets Followers Followees
Ashton Kutcher aplusk 3,205 3,407,385 209
Shaquille O’Neil THE_REAL_SHAQ 2,072 2,092,541 562
Stanley Kirk Burrell MCHammer 6,016 1,331,797 31,202
Sockington sockington 5,711 1,089,984 380
Justine Ezarik ijustine 7,718 605,441 3,039
News Outlets Username Tweets Followers Followees
CNN Breaking News cnnbrk 1,096 2,712,530 18 BarackObama 330 2,018,016 761,851 mashable 17,914 1,363,510 1,925
CNN cnn 11,607 193,625 50
Social Media Analysts Username Tweets Followers Followees
Gary Vaynerchuk garyvee 7,532 862,790 9,683
Chris Brogan chrisbrogan 48,341 94,715 88,431
Robert Scoble Scobleizer 23,112 94,295 2,423

The data that they mined was as collected over 10 days, in August 2009. The data included:

  • The 2143 tweets generated by the 12 users
  • The 90,130 actions (responses, RTs) triggered by the original 2143 tweets
  • All the tweets generated in connection with the 12 users (by their followers and friends;a total of 134, 654 tweets, 15,866,629 followers, and 899,773 friends/followees)

The authors produced 2 types of influence reports, based on the type of action that was triggered:

  1. conversational action (people replied, or mentioned the user – e.g. “meeting @stockington for catnip”)
  2. content-pushing action (people retweeted, or gave attribution – e.g. “via@username”)

Please note that a mention may or may not be a response to a tweet. If they were not responses to a tweet, they fall outside the authors’ definition of Twitter influence, and they should have been excluded from the analysis.

Here we go, on to the findings:

Conversational action

This graph shows you the amount of conversational activity (@replies and mentions) each user got in response to one (average) tweet.

Content action

This graph shows you how much content action (retweets and attributions) each user got for each (average) tweet:

So here we see that, per tweet, @sockington did get more retweets than @chrisbrogan.

The authors claim that these graphs of influence/tweet are the most accurate measure of Twitter influence so far. Therefore:

@sockington IS more influential on Twitter than @chrisbrogan,

because the fake cat gets more retweets. (sorry, @sockington, I do love you!!!)

I know exactly what you’re thinking, it starts with B and ends with T.

That’s because here we have a problem of construct validity. The measures do not actually measure influence. I wish the authors had read some research in communication & persuasion about the concept of influence, then worked their way from a conceptual to an operational definition.

Obviously, @sockington gets more retweets because he’s cuter & funnier than @chrisbrogan (sorry, Chris!). We don’t know why people reply or retweet. This study ignores a very important aspect of human relations: meaning. There is meaning in tweets, and meaning in why people retweet. But that is not captured in this study.

That being said, the report shows what can be done with data mining – it’s awesome! With a bit of help from people who know how to study meaning (hint, hint!), this type of research will be extremely valuable.

If anything, let this be an argument for computers & communication people working together, across disciplines.

In a future post, I will review conceptual and operational definitions of influence.

Quantum Physics

Someday, I will understand quantum physics. But since in the past few weeks I’ve been unpacking, unpacking, unpacking, unpacking, unpacking… (you get it)… OK, never mind. Here’s a video about quantum physics. It should be the beginning of any research methods class.

Thanks to Twitter user @c4chaos for pointing to a link that lead me to this video.

Two types of visibility

I just finished a webinar for PRWeb and talked about the research we did with SNCR about online news releases (pdf). I love Jiyan Wei (PRWeb product manager, he moderated the session) because he asks really good questions. One question he asked us today was to define visibility:

Everybody wants to gain visibility with news releases, but what is visibility?

I was lucky that Richard, my co-presented, was put on the spot first and I had a few seconds to think about this question 🙂

Here’s what I came up with, let me know if it makes sense to you:

I think about visibility as being of two types: push and pull, for lack of better terms.

Push visibility is the visibility you have when you “cut through the clutter” and your name (brand, product, etc.) makes headlines. People see it whether they want to or not. This is the type of visibility public relations and advertising have traditionally tried to achieve.

Pull visibility means being visibile and available when people need you and search for you. You might not be making headlines, but you are using the right keywords and showing up in relevant online searches. To use Richard’s company as an example, when people are looking for a Web development company in NY, Pillar should show up in the search results.

Traditionally, PR people have struggled to achieve push visibility, but given the changing landscape of media, of information availability, and information searching behaviors, for most of us, it is pull visibility that will make or break the bank.

In our survey results, people complained about not being able to cut through the clutter – not making headlines (i.e. not achieving push visibility). That’s OK. Not everybody can be in the headlines. As long as you are there for your audience when they need you, you’re OK.

What do you think? Does thinking about visibility in these terms help you?

The most important lesson

Back when I was a communication graduate student at Purdue, a friend asked me at a party:

So, what is the most important thing you know about communication?

I thought for a second (or two!) then I answered:

Know your audience.

Many years later, I still believe this is the most important lesson you can learn (and practice!) in communication – and of course, the related profession of public relations.

That’s why I’m happy to see posts such as this one by Todd Defren about Shift’s PR process, which starts with a lot of listening.

Carrie Woodward from Brains on Fire visited our class yesterday to talk about the Fiskateers community. It became apparent how much time and effort they put into getting to know their audience, and how they couldn’t have succeeded without extensive research and listening.

Yet, I see so many PR/marketing efforts that seem to be shots in the dark. Let’s just do this. Why? How? Oh, the details don’t matter. Let’s be on Facebook. Let’s be on Twitter.

I was trying to get the point across to my students, that you need to understand your audience, where they are, what they care about, what they talk about, and how… and I used this example:

Imagine you’re all sitting here in this classroom, waiting for PR class to start, but I walk in a random hall down the hallway and start lecturing there.

They laughed at the absurdity of the idea, yet how many companies do exactly that?

I hope my students will remember this lesson, and I hope they’ll be able to get it across to their bosses.

So there, that’s my most important lesson. What’s the most important thing you know about communication and PR?