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Delete selected tweets or likes with one click. Set up an auto delete process and we'll take care of the rest. 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 p. 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.
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.
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.
Panel a illustrates the average shift in peak activity observed for — by computing the difference between the pair of maps in Fig. 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.
The magnitude of Twitter behavioral shift following a Spring Forward event, averaged for the 4 years from to Texas is estimated to have experienced the greatest time shift. The effect of Spring Forward is more pronounced in the South, and center of the country. Alaska, Nebraska, and Hawaii have negative shifts.
The states most affected are Texas and Mississippi, where the shift was and 75 min respectively. Hawaii and Alaska are estimated to have negative shifts 15, and 30 min respectively. Twinflection shift produces similar spatial results to peak shift, with greater shift estimates.
This measurement reflects the ability of the data to capture the behavior of the tweeting population of each state. While Idaho, Wyoming, Montana, Utah and South Dakota have relatively little data compared to their populations, the remaining states have similar data density, with somewhere between five and eleven tweets per thousand residents, with the exception of the District of Columbia which has Note: both panels c and d use logarithmically spaced colorbars.
Figure 4 b 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 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. 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.
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 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 Plots for the other 3 years exhibit similar behavior. Before Spring Forward red , most states show a peak between and p.
The week of Spring Forward blue , nearly all states have a peak after p. 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.
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. Normalized Twitter activity between 12 p. Monday prior to and following the Spring Forward event for each state. Red indicates an aggregation of data from the specified period over 4 weeks before the Spring Forward Event.
Blue indicates data from the single 24 h period after Spring Forward has occurred. Texas exhibits the largest change following Spring Forward. Curves for nearly all states have aligned by Monday morning.
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 million individuals in the U. Eastern, during the second half of the football game. The map in Fig. 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.
Peak activity time local for Super Bowl Sunday, 5 weeks prior to Spring Forward, averaged over the years to Activity exhibits a clear resemblance to the U. Eastern Time just following the halftime performance. The data suggests a national collective synchronization in attention. Green Bay Packers d. Pittsburgh Steelers , New York Giants d.
New England Patriots , Baltimore Ravens d. San Francisco 49ers , and Seattle Seahawks d. Denver Broncos We note that the colormap here the same as the scale used for 3 , with blue colors included to reflect the relatively early times of the peaks relative to the other weeks.
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. Technically speaking, Spring Forward occurs very early Sunday morning, and the instantaneous clock adjustment from 2 a. In addition, we speculate that the majority of individuals do not set an alarm clock for Sunday morning.
As a result, we expect that the hour lost to Spring Forward will be felt by our bodies most meaningfully on Monday morning. Indeed, we are likely to experience the Monday morning alarm as occurring an hour early, as Spring Forward shortens the time typically reserved for sleep opportunity Sunday night by 1 h. Considering the correlation between screen time and lack of sleep, the Sunday evening shift, and the corresponding Monday morning re-synchronization, we observe evidence that sleep opportunity is lost in some states on the evening of Spring Forward.
By estimating the magnitude and spatial distribution of the shift in Twitter behavioral curves, we have approximated a lower bound on sleep loss at the state level.
Our pair of measurement methodologies have a Pearson correlation coefficient of 0. While they produced slightly different estimates of the magnitude of temporal shift in behavior, the resulting geographic profiles of sleep loss were similar. Both suggest that states along the coast are least affected by Spring Forward, while Texas and the states surrounding it to the North and East are the most affected.
Peak shift suggests the temporal shift in behavior due to Spring Forward generally less than the actual clock shift 1 h. California, the state for which we have the most data and therefore the most representative behavior profile after smoothing, was found to have a peak shift of 30 min. Considering the clock adjustment of exactly 1 h, both measurements are plausibly directly representative of sleep lost, however the differing magnitudes of the measurements indicate that future work should clarify the relationship between these measurements and actual shifts.
Twinflection measured similar shifts for most states, but for a few estimated larger effects. While California was measured as having the same 30 min shift, Texas, the state for which we have the second most data, was estimated by twinflection to be delayed by an additional 45 min.
Twinflection measured a small forward shift for the state of Arizona, which does not observe DST. This could indicate that the twinflection method overestimates the behavioral shift. It is also possible that a shift in behavior could occur for residents of Arizona, as a result of their connections to those in neighboring states, and in their former timezone.
In example, some residents likely work in bordering states, and are forced to observe DST, and some will likely engage in more online activity and discussion when their peers are present- those peers being initially established by a shared time of activity.
This we believe to be an important distinction between Arizona and Hawaii, which also does not observe DST. Hawaii is measured to have gained sleep opportunity by both accounts. Lacking the observation of DST, neighboring states, and other states in the same timezone, it is plausible that behavior in Hawaii would be unlike any other state, and be more independent of behaviors in other states. This sparsity of data and relative independence from other states is shared with Alaska, the other state with a measured sleep opportunity gain by both measures.
These states have smaller populations, less population density, and lower volume of tweets. As a result, the behavioral curves associated with these states are less reliable. Discrepancies in available data were determined to be largely accounted for by differences in population. Thus, we expect results for each state exclusive of those mentioned earlier to be comparably reliable in their representation of sleep loss for the state as a whole.
Incremental future work in this area could investigate state specific sleep loss related to Spring Forward events, which would allow further clarification of the relationship between the magnitude of behavioral shifts on Twitter and population sleep loss. Other directions might include looking at other sleep opportunity interruption events such as the end of Daylight Savings in November, where we are ostensibly given an additional hour of sleep opportunity.
This and other works would also benefit from exploration of the relationship between measurements of sleep opportunity as given by social media activity and actual sleep duration. More ambitiously, proxy data such as this could be verified by matching wearable measurements of sleep e. Fitbit with social media accounts. Our study suffers from several limitations associated with our data source, we describe a few such examples here.
The geographic location users provide in their Twitter bio is static and unlikely to be updated when traveling. As a result, user locations time zone, state inferred from this field will not always reflect their precise location. The GPS tagged messages included in our analysis will not suffer from this same uncertainty. Furthermore, the tweeting population of each state is likely to have complicated biases with respect to their representation of the general population [ 50 ].
Our dataset likely contains automated activity. Indeed, an entire ecology of algorithmic tweets evolved during the period in which we collected data for this study. However, we expect the majority of this activity to be scheduled using software that updates local time automatically in response to Daylight Savings.
As we showed for the Super Bowl, live televised events e. Indeed, many individuals take to Twitter as a second screen during such events to interact with other viewers. In addition, streaming services such as Netflix and HBO often release new episodes of popular shows on Sunday night to align with peak consumption opportunity.
These cultural attractions exert a temporal organizing influence on our leisure behavior, and the Spring Forward disturbance translates this synchronization forward in time. It is worth noting that early March is a rather dull time of year for popular professional sports in the United States.
Her famous selfie tweet containing many famous actors was posted that evening, a message which held the record for most retweeted status update for several years [ 51 ].
The event happened the week before Spring Forward, and led to anomalous behavior compared with all other Sundays we looked at. Since Spring Forward only occurs once per year, the specific language of the tweets is highly dependent on events occurring on that specific day. The variability in daily events and susceptibility of affect to these daily events makes study of the actual language in the tweets unreliable. Finally, Twitter and other social media companies have access to much higher fidelity information regarding user activity than we have analyzed here.
We are not able to analyze consumption activity on the site, e. These forms of interaction with the Twitter ecosystem are likely to occur chronologically following the final posting of a message in the evening, and prior to the initial posting of a message in the morning. As a result, we expect our estimate of the sleep opportunity lost due to Spring Forward to be a lower bound.
Privacy preserving passive measurement of daily behavior has tremendous potential to transform population-scale human activity into public health insight. The present study leverages a natural experiment in sleep loss to identify behavioral adaptation from Twitter data. Which cities in the U. Which states are increasingly suffering from insomnia? Answers to questions like these are not available today, but could lead to better public health surveillance in the near future.
For example, communities exhibiting disrupted sleep in a collective pattern may be in the early stages of the outbreak of the flu or some other virus. Current methodologies for answering these questions are not scalable, but social media, mobile devices, and wearable fitness trackers offer a new opportunity for improved monitoring of public health.
Using samples of tweets from the same time periods will generate similar results. Joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society on the recommended amount of sleep for a healthy adult: Methodology and discussion.
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