Our overall finding is that peak Twitter activity occurs 15–30 min later on the Sunday evening immediately following Spring Forward for most states, with this shift varying among states. By Monday morning, activity is back to normal, suggesting that the window of sleep opportunity is visibly compressed in Twitter behavior.
In Fig. 1, we plot B(t) for the subset of posts containing the words ‘breakfast’, ‘lunch’, and ‘dinner’ for the period beginning 6 a.m. on Sunday and ending 9 p.m. on Monday, both before (solid) and the weeks of (dashed) Spring Forward events. These curves were constructed for states observing Eastern Time (top row) and Pacific Time (bottom row). These regions were chosen as they are the zones with the greatest spatial difference among zones with significant data density. Observing a shift in behavior for each assures us that these shifts are not limited to a particular geographic region of the country.
Meal-related language reveals a daily pattern of behavior in which peak volume occurs around the time that meal typically takes place. On an average Sunday, breakfast is most mentioned at 10:30 a.m., lunch at 1:15 p.m., and dinner at 6:45 p.m. in Eastern Time Zone states (see Fig. 1). On the average Monday, breakfast mentions peak at 10:45 a.m., lunch peaks at 1:30 p.m., and dinner at 7:15 p.m. Breakfast and Lunch are mentioned more often on Sunday than on Monday.
There is essentially no discussion of meals during the period from 2 a.m. to 6 a.m. These plots also exhibit a small forward shift in time following Spring Forward, suggesting that each meal was tweeted about, and probably eaten, later in the day on Sunday. The effect is greater on the East Coast, and disappears on both coasts by Monday.
Broadening from messages mentioning specific meals to all messages, daily activity plots of \(B_{BSF}\) and \(B_{SF}\) reveal a regular diurnal pattern of behavior that is consistently shifted forward in time the evening following Spring Forward events. Figure 2 shows this shift for the year 2013, but the results were similar for other years.
Panel (a) suggests overall activity across the U.S. peaks around 9 p.m. on Sundays before Spring Forward (red circles), and experiences a minimum around 5am. The peak shifts approximately 45 min later on the Sunday of Spring Forward (blue squares) before synchronizing again by early morning Monday. In panel (b) California is used as an illustrative example of these patterns existing at the state level, and the smooth behavioral pattern constructed using Gaussian Process Regression. The pattern is similar to that observed for the entire country, with the exception of a slightly reduced amplitude. Twinflection points are illustrated by black squares in panels (b) and (c).
Figure 2 demonstrates evidence that there is a shift in the peak time spent interacting with Twitter on Sunday evening following Spring Forward, relative to prior Sundays. Given the absence of a corresponding delay in interaction Monday morning, we infer a decrease in sleep opportunity experienced on Sunday night.
To explore the spatial distribution of the behavioral changes induced by Spring Forward, in Fig. 3 we map the time of peak Twitter activity on Sunday night for each state before (top) and the week of (bottom) Spring Forward, averaged across the years 2011–2014. On the Sundays leading up to Spring Forward (top), peak twitter activity occurs near either 10 p.m. for states on the East Coast, or 9:15 p.m., for most of the other states. The week of Spring Forward, nearly all states exhibit peak activity later in the night.
Looking at Texas as an individual example, before Spring Forward we see peak activity around 9:15 p.m. local time, and the week of Spring Forward it occurs at 10:15 p.m. local time. While Texas is one of the latest peaks observed on the evening following Spring Forward, several other states are up late as well including Oklahoma, Georgia, and Mississippi each peaking around 10:15 p.m.
In the Additional file 1, we show maps estimating the time of peak activity for each of the individual 9 weeks centered on Spring Forward (see Additional file 1: Fig. S1). There is some week-to-week variation, most notably in the second week prior to Spring Forward, which was the night of the Academy Awards for three of the 4 years. By 4 weeks after Spring Forward, the peak activity map has relaxed to roughly the same pattern as BSF.
The magnitude of the forward shift in behavior illustrated in Fig. 3 is considered a proxy for the loss of sleep opportunity on the Sunday night following Spring Forward.
We used two distinct methods to estimate this magnitude, namely the peak shift and the twinflection shift. A comparison of the spatial estimates made using each method are shown in Fig. 4.
Panel (a) illustrates the average shift in peak activity observed for 2011–2014 by computing the difference between the pair of maps in Fig. 3 (bottom minus top). There is clear spatial variation in the shift in time on the night of Spring Forward, while most states exhibit a positive forward shift some exhibit none, and Alaska, Hawaii, and Nebraska show a negative shift. The peak in Twitter behavior for the east and west coasts occurred 0–30 min later Sunday night, while it occurred 30–60 min later for the central U.S. (Fig. 4a).
Figure 4b estimates the change using twinflection, namely the change in concavity of the behavior activity curve from down to up. Every state except Hawaii, Alaska, and Wyoming exhibits a shift forward in time, and with similar spatial regularity. When measured with twinflection shift, Texas and Mississippi are seen to have the greatest temporal shift following Spring Forward. Texans were tweeting 105 min later than usual following a Spring Forward event. Most of the east and west coast states were measured as tweeting 15 to 30 min later (Fig. 4b). Both measures agreed on a positive shift for the country as a whole. However, the two measures yielded different results for the magnitude of these shifts, with twinflection shift generally estimating a more positive shift.
Figure 4c, d illustrate the amount of data contributing to calculations for the behavioral curves, and the density of this data with respect to each state’s population. Idaho, Alaska, Hawaii, Montana, Wyoming, North Dakota, South Dakota, and Vermont were the states offering the smallest amount of data, and subsequently have the highest potential for a poor behavioral curve model fit. Wyoming was unique in that in 2013 for the 24 h observation window on the week of Spring Forward there were no tweets meeting inclusion requirements, making conclusions about this state particularly tenuous.
Though the amount of data available for California and Texas is much greater than the other states, when considering their large population size we find their twitter activity per capita to be similar to most other states. Based on our estimate of tweets per capita, we expect behavioral curves for most states to be more or less equally representative of their tweeting populations.
Looking at the diurnal cycle of Twitter activity for each individual state, we see remarkable consistency. Figure 5 shows the 24 h period spanning noon Sunday to noon Monday local time for the year 2012. Plots for the other 3 years exhibit similar behavior. Before Spring Forward (red), most states show a peak between 9:15 and 10:00 p.m., local time. The week of Spring Forward (blue), nearly all states have a peak after 9:30 p.m. While states differ slightly in the time of peak, and magnitude of shift in the peak, most exhibit a clear positive shift (see Additional file 1: Fig. S3). By Monday morning, nearly all curves have re-aligned. We also consistently observe higher peaks for the BSF curves which we believe to be driven by televised events such as the Oscars. The Sunday of Spring Forward does not have a regularly scheduled popular television event, and as a result the SF curves have lower amplitude.
Both the peak and twinflection demonstrate that it is possible to observe a measurable decrease in the amount of sleep opportunity people in the United States receive on average due to Spring Forward. They also both demonstrate uneven geographic distribution of the effect of Spring Forward, and therefore the ability to determine geographic disparity in sleep loss.
We also discovered that the Super Bowl occurred exactly 5 weeks prior to Spring Forward in each of the years studied. This annual event watched by over 100 million individuals in the U.S. caused peak Twitter activity to synchronize at roughly the same time nationally, around 9 p.m. Eastern, during the second half of the football game. The map in Fig. 6 shows the time of peak activity for each state on Super Bowl Sunday, averaged over the years 2011 to 2014. The colormap is the same as the scale used for 3, with the additional cooler range brought in capture the time of peak relative to the usual times.
The map bears a remarkable resemblance to the timezone map, demonstrating a synchronization of collective attention across the country. Data from Super Bowl Sunday was not included in the Before Spring Forward data, as it does not accurately reflect the spatial distribution of typical posting behavior on a Sunday evening.