Recently, I happened to notice a pattern of recycled content on The New York Times’ Twitter feed. I decided to take a look at one particular story to see if I could make sense of the pattern.
The story is a quiz titled “Can You Tell What Makes a Good Tweet?” that is itself a quiz from a study done at Cornell by CS folks (and apparently with cooperation from one author at Google). I took the quiz, and despite my research on Twitter, the algorithm they developed for predicting a retweetable tweet won out (not surprising since it had a lot more practice and doesn’t get bored with making picks like I did).
I was struck by the irony of reporting on an algorithm that can pick the winning tweets done by an agency that is trying to float that report as clickbait, so I compiled a list of the @nytimes tweets promoting this quiz. I did it without accessing the Times or Twitter APIs, but it would be interesting for Comm or Journalism folks to do a study of recycling behaviors on media and other outlets. Unlike the Cornell study (which compared two tweets posting the same link at different times) the tweets I’m comparing are exactly the same (same content, same image, same link). Here’s what I came up with:
Total tweets of this article (and associated image): 7
Days from first to last tweet: 5
First tweet (all subsequent tweets are identical):
— The New York Times (@nytimes) July 2, 2014
|8:26 AM – 2 Jul 2014
|11:31 AM – 2 Jul 2014
|10:13 PM – 2 Jul 2014
|4:02 AM – 3 Jul 2014
|6:42 PM – 3 Jul 2014
|9:59 PM – 4 Jul 2014
|10:43 PM – 5 Jul 2014
*NOTE: all times in CDT
My extremely unscientific study shows a couple of interesting things:
- Look how the RT and Favorite numbers dip for the morning tweets on 7/2 and 7/3. Perhaps the 7/2 tweet came too close to the initial tweet. Was the 7/3 tweet meant for early risers / European readers?
- The numbers seem to attenuate until you get to the evening July 4th/5th tweets; on a holiday weekend, are people done hanging with the relatives and scanning Twitter before bed?
As I went progressed in the quiz, I noticed that time of day was an important predictive factor for my guesses; as far as I can tell, the Cornell authors didn’t consider that at all (only the time between tweets). If no one is awake or your tweet doesn’t come to the top of the heap some other way (through RTs, hashtag coincidence, your presence in a list, etc.) my guess is it might as well be gone. There are so many factors independent of tweet content that can influence propagation (including holiday weekends where folks retreat to their separate bedrooms and mobile devices after a long day of family bonding).
As I operate on the assumption of agency and strategy in SM use by organizations, I’m guessing the times these tweets went out weren’t accidental. I think that the social media folks at the NYT are thinking about how often and when they want to tweet out content, and possibly even adjusting their strategy before they retire that content from their SM streams. Based on the tweet I looked at, however, it’s hard to guess what that strategy is.
The Cornell study raises some interesting questions about how to predict tweets that will maximize content dissemination, but also misses some of the many complicated factors that go into predicting proliferation.