How “smart” is your smart TV?

image: TV by Anuar Zhumaev http://thenounproject.com/term/tv/99975/, CC-BY 3.0

This recent Daily Beast news article has particular relevance to me as we’ve recently bought one of these Samsung tellies (although nowhere near as humongous as the one in this picture from the equivalent BBC article!).

The idea of “smart devices” is quite a cool one, and appears quite benign. Look, my telly is remembering the stuff I watch and making life easier for me.

Except the TV isn’t the thing that’s smart. The “smartness” of the device isn’t contained within the machine, it come from the data centre that holds all the information we give it. The data about my watching habits is collected by that frightfully useful  internet connection (on a TV! Imagine!) that transfers the information back to the TV manufacturer or another 3rd party so they can provide the services they think I will find most appealing, encourage me to use the device more and give them another product they can sell in the data about my family’s behaviour.

Now, I’m not going to be sending my TV back. I’m not shocked or surprised by the news story. I might turn off voice recognition which I never used anyway. It’s a really cool TV especially after the little bucket of visual fuzz we had previously.

But this goes back to my last post about being critical about the technology we use. Do we as a society understand enough about how our data is collected and exploited? The fact that the news article has gained such a lot of traction today suggests not.

TV’s have been a part of our living rooms for many decades and we’ve got used to their status as receivers of information. We now have to adjust to the idea that they are now computers and we should think about them as such.

As we grow accustomed to a world where more of our devices are connected (wifi kettle, anyone?), understanding this becomes an ever more important part of digital literacy.

Learners and technology: enabled but not empowered?

image: “_D3S9849” by US Embassy Tel Aviv https://flic.kr/p/bDjjUL CC-BY-SA

Something’s been bothering me recently. As learning technologists, people advocating the use of technology within education, are we helping learners to be sufficiently critical of the tools they are using?

I hope I’m not over simplifying, but much of the critique around using web-based tools, particularly social media,  focuses on issues of function and safeguarding. We talk less about examining the background, particularly the business model, of apps or services to determine whether using them is in the best interests of the individual.

I recognise I’ve been as guilty of this as anyone. We’re surrounded by free tools and apps and I’ve been eager to share these and to see how they shape the way people learn.

The implications of this were brought home to me a few months ago when I replaced my phone. Rather than backup from my previous phone’s settings I decided to do a digital life laundry and reinstall only the apps I actually needed.

If you try this you’ll know that you get regularly asked to accept that an app would like access to certain things on your phone like your camera or microphone but also your contacts list, location and so on. What they don’t always specify is why they need this.

A human rights issue?

Of course, it’s not news to say that what these apps are after in many cases is your data because that is a commodity the app producer can sell. I was encouraged to think about this differently by Aral Balkan at last year’s Thinking Digital conference.

For him, the issue isn’t just about privacy. It’s about human rights. To hear him explain why, watch the video. It’s worth 20 minutes of your time.

Aral Balkan – Free is a Lie – Thinking Digital 2014 from Thinking Digital on Vimeo.

It was with this in mind that I delivered a lecture recently to foundation year engineering students on digital citizenship. I used the example of Uber to highlight the issues around the divergence of the needs of the business and the rights of the individual.

I might also have talked about the ethical minefield of Facebook and their “emotional contagion” experiment.

I’m not advocating that we retreat from using free tools; that would be ridiculous. On balance, having access to a powerful set of tools like Google Docs or Dropbox in exchange for access to our data may work strongly in our favour. But I do think that “following the money” should be an integral part of what we think about when choosing these tools and deciding how to use them. If it’s a bargain we’re willing to strike when we start using them then that’s fine. What we should avoid is blindly accepting the EULA as this Guardian article on controlling personal data discusses.

Why worry about it?

This is important for individuals. Technology is becoming more and more intimate. With the emergence of wearable technologies and the “internet of things”, the data about our daily existence is becoming much more nuanced and granular, data that belongs to organisations, not individuals. And information that is filtered through algorithms and presented back to us shapes our world view and behaviours. This was highlighted by people comparing the simultaneous trending topics on Facebook and Twitter during the Ferguson protests.

It’s also something we need to think about as institutions. In order to participate fully in education, do we require learners to subscribe to services run by organisations that operate in ways that are not necessarily in the best interests of the people we have a duty of care towards?

We should encourage learners to be as constructively critical of the nature of the technologies they use as the  academic literature they read.

We may be enabling students to use technology but are we empowering them? Current patterns of demand will shape the development of future technologies and if we fail to encourage learners to be critical then we do them a disservice and risk creating a technology landscape that we may one day come to regret.

Twitter Analytics arrives, but just because you can measure something…

Originally published on Netskills Voices

Twitter has recently rolled out a new Analytics tool to the majority of its users so you can now see a little bit more about what happens in the life of one of your tweets. Is this going to be useful information for you?

Twitter Analytics screengrab

Gauging the impact of your use of Twitter has, for most people, been a bit of a hit and miss affair. What do you measure? Total number of tweets, replies, RTs or favourites? These have always been quite crude measures.

Until now, the more sophisticated tools have required either detailed technical knowledge or have come with a significant cost attached. Twitter’s Analytics service seems to fill a gap between what’s easily available for free on their main site and the more commercial tools.

Getting started

This couldn’t be simpler, really. Go to http://analytics.twitter.com and sign into your Twitter account if you haven’t already done so.

If it all looks a bit empty when you get there, it’s because it only activates when you first visit the site. No retrospective data is available. Tweet away and you’ll find the data starts to appear.

What does it tell you?

For each tweet (only manual RT’s show up) you can see how many impressions you made and what level of engagement each tweet had. An impression means simply that a tweet has been delivered to someone’s Twitter timeline. It doesn’t mean they have actually seen it.

The engagement measure is a bit more interesting. This means that a Twitter user has done something with your tweet. Drilling down by clicking on this figure shows you that this can include things like:

  • Replies
  • Retweets
  • Favourites
  • Link clicks (if there is one in the tweet)
  • Hashtag clicks
  • Embedded media clicks…
  • …and a few others

Twitter Analytics screengrab

The most potentially useful piece of data you can get from the tool is the engagement rate, a simple measure of the number of engagements divided by the number of impressions. This means you can say things like “well only 50 people saw that tweet but I know that 63% of them clicked the link to our website” which is much more data than you’ve been able to get this easily before.

There’s a handy bar chart showing variable activity over the last 24 hours. The whole thing is fairly intuitive and well presented.

If you want to do more interesting data analysis you can export the data as a CSV file.

You can also get a profile of your followers showing gender, location and “interests”. There is little indication how these interests are worked out. Education is high on the list for my followers as you’d expect. Apparently 29% of my followers like “comedy”.

It’s also restricted to your own tweets, replies and promoted tweets. If you want to track a conference hashtag then there’s not much for you here.

Quantity or quality?

[SPOILER: Quantity]

If you’ve used something like Google Analytics you’ll notice that there’s a wealth of data that just isn’t available here. For example, there’s no indication of how or where people are engaging with your individual tweets. It might be interesting to see the geographical reach of a single tweet or perhaps what devices people are viewing it on.

One of the things we emphasise on our blogging workshop is that this quantitative data isn’t the be all and end all of social media.

It tells you nothing about the quality of your interactions with people or organisations. You could be the most outrageous troll sporting a fantastic engagement rate but leaving discussions, reputations or emotions in tatters in your wake.

Additionally, number of impressions or engagement rates may not even be important information for you. As an individual, I don’t use Twitter to have loads of followers, RT’s etc. I use it to have and follow conversations, the value of which isn’t determined by my engagement rate.

Is the data valuable?

Why is Twitter making this available now? That’s unclear but it could be argued that if people can see what happens with a tweet it’s more likely to encourage increased use with all the benefits in data and monetisation that you’d expect Twitter to want.

But does the user get enough benefit to balance that out? Although there are now more numbers to look at, the data is still pretty crude and requires proper interpretation (or leaps of imagination!) to derive much meaning from it.

The tool certainly gives you more information than has been available through Twitter in the past. The additional data might be useful if you are trying to set targets for an organisation’s Twitter account or need ammunition to convince others that use of Twitter is worthwhile.

So, have a look if you’re responsible for managing your team’s Twitter account, are in charge of the marketing for an event or are an individual curious to get a better picture of what happens after your click the Tweet button. There might be some useful insights in there.

But don’t be seduced by the lure of lots of numbers if they’re not actually that important to you! Just because you can measure something doesn’t mean that it’s actually worth measuring.

Many thanks to @catherinelliott, @hopkinsdavid, @bhanwar, @ntaylorHEA, @philswinhoe and @carlvincent for being sports and boosting my engagement rate so I had some numbers to look at. 🙂

 

#Infographics and the art of political persuasion

We’ve been talking about infographics a bit at Netskills towers recently and one of the discussion points was the difference between clarifying data by presenting it in particular ways and spinning data to get across a message.

This came up on the Information Aesthetics blog today.

Read the post for a bit of background. 

Good aspects to reflect on might be:

  • What is the impact on the viewer of pairing the video clips with the data? What happens when you see them in isolation?
  • Why were these types of graphics chosen? Also, consider the type face, colour scheme etc of the graphics framing the data.
  • What’s the provenance of the data? (Sources are shown on the accompanying Flickr image)
  • What alternative data might be included?
  • What are other ways of presenting the same data in different ways that communicate a different message?
  • What range of factors are at play that might contribute to the changes shown here? 
  • To what extent is any US president responsible for these types of data?
  • How persuasive do you find this?

Also, who is presenting this information? Following the embed back to YouTube you find it’s a group called the Minnesota Majority, “a non-profit grassroots advocacy group working to promote traditional values in public policy” (from their YouTube channel About Me section). What else might we want to discover about this group?

I’m not interested in the political debate here. It’s just a great starting point for a discussion about stories, data, politics and persuasion.

What other open questions would you want to ask students about this video?