Posts filed under Social Media (41)

March 26, 2014

Are web-based student drinking interventions worthwhile?

Heavy drinking and the societal harm it causes is a big issue and attracts a lot of media and scholarly attention (and Statschat’s, too). So we were interested to see today’s new release from the Journal of the American Medical Association. It describes a double-blind, parallel-group, individually-randomised trial that studied moderate to heavy student drinkers from seven of our eight universities to see if a web-based alcohol screening and intervention programme reduced their unhealthy drinking behaviour.

And the short answer? Not really. But if they identified as Māori, the answer was … yes, with a caveat. More on that in a moment.

Statistician Nicholas Horton and colleagues used an online questionnaire to identify students at Otago, Auckland, Canterbury, Victoria, Lincoln, Massey, and Waikato who had unhealthy drinking habits. Half the students were assigned at random to receive personalised feedback and the other students had no input. Five months later, researchers followed up with the students on certain aspects of their drinking.

The overall result? “The intervention group tended to have less drinking and fewer problems then the control group, but the effects were relatively modest,” says Professor Horton. The take-away message: A web-based alcohol screening and intervention program had little effect on unhealthy drinking among New Zealand uni students. Restrictions on alcohol availability and promotion are still needed if we really want to tackle alcohol abuse.

But among Māori students, who comprise 10% of our national uni population, those receiving intervention were found to drink 22% less alcohol and to experience 19% fewer alcohol-related academic problems at the five-month follow-up. The paper suggests that Māori students are possibly more heavily influenced by social-norm feedback than non-Māori students. “Māori students may have a stronger group identity, enhanced by being a small minority in the university setting.” But the paper warns that the difference could also be due to chance, “underscoring the need to undertake replication and further studies evaluating web-based alcohol screening and brief intervention in full-scale effectiveness trials.”

The paper is here. Read the JAMA editorial here.




March 21, 2014

Common exposures are common

A California head-lice treatment business has had huge success in publicising its business with the claim that selfies are causing a  rise in nits among teenagers. The Herald mentions this in Sideswipe, the right place for this sort of story, but other international sites have been less discriminating.

There are no actual numbers involved, and nothing like representative data even if you’re in the South Bay area of central California. More importantly, though, there is no comparison group. The owner of the business, Mary MacQuillan, says “Every teen I’ve treated, I ask about selfies, and they admit that they are taking them every day.”  That’s probably only a slight exaggeration at most, but every teen she hasn’t treated has also probably been taking photos that way. It’s something teenagers do.  Common exposures are common.

So, why were news organisations around the world publicising this? The fact that it’s about teenagers and the internet goes a long way to explaining it.  It doesn’t need evidence because teenage use of technology is automatically scary and newsworthy: as Ms MacQuillan says ” I think parents need to be aware, and teenagers need to be aware too. Selfies are fun, but the consequences are real.”

You get the same thing happening with ‘chemicals’, as the dihydrogen monoxide parody website loves to point out

A recent stunning revelation is that in every single instance of violence in our country’s schools, …, dihydrogen monoxide was involved.


March 18, 2014

Big Data & privacy presentation

If you have time, there’s an interesting event that will be streamed from New York University this (NZ) morning (10:30am today NZ time, 5:30pm yesterday NY time)

..the Data & Society Research Institute, the White House Office of Science and Technology Policy, and New York University’s Information Law Institute will be co-hosting a public event entitled The Social, Cultural, & Ethical Dimensions of “Big Data.” The purpose of this event is to convene key stakeholders and thought leaders from across academia, government, industry, and civil society to examine the social, cultural, and ethical implications of “big data,” with an eye to both the challenges and opportunities presented by the phenomenon.

The event is being organised by danah boyd, who we’ve mentioned a few times and whose new book I plan to write about soon.

February 27, 2014

As if you didn’t have enough to read

A new blog of science-themed links and (NZ) event listings, Science Club.

Their most recent  post is for this story from the Guardian, which reports that one in every thirteen tweets contains swearing.

What do you think is the most commonly used swearword on Twitter? Well of course it is

There is, of course, substantial variation between users. Most of the people I follow are dragging the average down.

February 26, 2014

Caricatures in music space

There’s a map going around Twitter, being described as the most popular band in each US state

It’s a bit surprising that every state has a different favourite band, so I looked at the site listed on the map as the source.  In fact, the listed bands are not the most popular ones in any of the states. They are something more interesting.

Paul Lamere used Spotify (and perhaps other social music-streaming services) to get music listening preferences for 200000 people. He then looked at which artist in the top 100 for a state had the worst ranking over the US as a whole. He forced the result to be different for every state by bumping the less-populous state to its next choice when there was a tie. So, as the title on the map actually says, these are the most distinctive bands for a state, not the most popular.  They are caricatures, not photographs.

Since he had data based on postal code (ZIP code), it’s a pity he grouped these all the way up to the state level.  It would have been interesting to see urban vs suburban vs rural differences, and the major geographical trends across states such as Texas.

January 16, 2014

Private-sector surveillance

A maths-free article on data mining and surveillance, from the New York Review of Books

Using techniques ranging from supermarket loyalty cards to targeted advertising on Facebook, private companies systematically collect very personal information, from who you are, to what you do, to what you buy. Data about your online and offline behavior are combined, analyzed, and sold to marketers, corporations, governments, and even criminals. The scope of this collection, aggregation, and brokering of information is similar to, if not larger than, that of theNSA, yet it is almost entirely unregulated and many of the activities of data-mining and digital marketing firms are not publicly known at all.

January 9, 2014

Infographic of the week

Via @keith_ng, this masterpiece showing that more searches for help lead to more language. Or something.


It’s not, sadly, unusual to see numbers being used just for ordering, but in this case the numbers don’t even agree with the vertical ordering.  And several of them aren’t, actually, languages. And the headline is just bogus.

This version, by Kevin Marks (@kevinmarks), at least is accurate and readable.


but it’s hard to tell how much of Java’s dominance is due to it being popular versus being confusing.

Adam Bard has data on the most popular languages on the huge open-source software repository GitHub. This isn’t quite the right denominator, since Stack Overflow users aren’t quite the same population as GitHub users, but it’s something.  Assigning iOS, Android, and Rails, to Objective-C, Java, and Ruby respectively, and scaling by GitHub popularity, we find that C# has the most StackOverflow queries per GitHub commit; Objective-C and Java have about two-thirds as many.  In the end, though, this data isn’t going to tell you much about either high-demand programming skills or the relative friendliness of different programming languages.



November 25, 2013

–ing Twitter map

Showing what can be done straightforwardly with online data, the site (possibly NSFW) is a live map of tweets containing what the Broadcasting Standards Authority tells us is the 8th most unacceptable word for NZ.  Surprisingly, it was written by a Canadian.



November 19, 2013


  • Animated visualisation of motor vehicle accident rates over the year in Australia. Unfortunately it’s based on just one year of data, which isn’t really enough. And if you’re going the effort of the animation, it would have been nice to use it to illustrate uncertainty/variability in the data
  • Randomised trials outside medicine: the combined results of ten trials of restorative justice conferences. Reoffending over the next two years was reduced, and the victims were happier with the handling of the case. (via @hildabast)
  • How much do @nytimes tweets affect pageviews for their stories?
October 18, 2013

Is the King (of beers) no longer the king?

Anecdotally, many of the New Zealanders I talk to think that a) all American beer is appallingly bad, and that b) this is all that Americans drink. In fact, the US has been leading the micro- and craft- brewing revolution for some years now, and a new survey shows that American beer drinking tastes are changing. Budweiser, the so-called King of Beers, a product of US brewing giant Anheuser Busch, appears to have been deposed by Colorado based Blue Moon Brewing Company. I am sure someone will tell me that far more Budweiser/Millers/Coors is produced than beer from Blue Moon, but hey maybe American’s are just using it to pre-cook bratwursts before grilling like I used to do.

I was a little concerned that this study might be self-selected, or industry motivated, but the information provided gives some reassurance: “Data on behalf Blowfish for Hangovers by a third party, private research firm based on a study of 5,249 Americans who drink alcohol and are over the age of 21. Margin of error for this study is 1.35% at a 95% confidence interval. Additional data on alcoholic beverage sales collected directly by the Alcoholic Epidemiolic Data System (AEDS) from States or provided by beverage industry sources.”