1. The Seasonality of Belief:
How Temperatures in the U.S. Affect Public Interest in Global Climate Change
S. J. RALSTON
EAS 4480

2. Goals and Motivation
Wanted to investigate how the temperature outside affected people's belief in global warming
Since we can't directly measure belief, chose to measure interest instead
Motivated primarily by comments made during "Snowpocalpyse" this past winter
e.g. "Look at all this snow! So much for Global Warming."

3. Methods Obtained regional search data from the U.S. from Google Trends
In the process of obtaining U.S. temperature data from the National Climatic Data Center, a department of NOAA
They don't want to give me the data and haven't explained why yet
If no temperature data can be obtained, will simply compare search data to season, or a temperature proxy

4. Methods, cont.
Search data is provided on a weekly basis, but temperature data (if/when it arrives) is on a monthly basis
i.e. The search volume is measured once per week, while the temperature is measured once per month
Therefore, interpolated the temperature data for once-per-week sampling frequency
Examined search terms "global warming" and "climate change" versus temperature using Welch's Method

5. Methods, cont.
Looking at periodogram, phase lag, and coherence of the data
Until real temperature data comes in, using a sine function oscillating between +70 and +30 degrees Fahrenheit with a period of 6 months.
Values of +70/+30 were chosen based on national averages from
Search data covers a range of 120 months (from Jan Dec. 2013), so using matching temp. data

6. Search Volume: A Caveat
From Google Trends:
"Numbers represent search interest relative to the highest point on the chart. If at most 10% of searches for the given region and time frame were for "pizza," we'd consider this 100. This doesn't convey absolute search volume."
In other words, we know only how much interest there was in "pizza" relative to the time there was the most interest in pizza.

7. Google Trends: Relatively
From

8. Google Trends: For Reference...
From

9. Preliminary Results
Although temperature data has not yet been acquired, an early look at periodicity—coupled with a false temperature vector—has been informative
Seems already to have a six-month periodicity
The question (which we will answer with the real temperature data) is whether high or low temperatures are correlating with the changes in search volume, and if we can improve upon the coherence observed

10. Preliminary Results, cont.
X: weeks, Y: temp and search volume
X: 1/weeks, Y: cross-PSD estimate

11. Preliminary Results, cont.
X: 1/weeks, Y: Coherence Estimate
X: 1/weeks, Y: PSD Estimate by FFT

12. Preliminary Results, cont.
Phase lag
For "global warming," up to +0.5 weeks
For "climate change," up to +0.5 weeks also
Significant periodicity observed at 6 months (27 weeks, to be exact), but this is likely due to overbearing influence of regular sinusoidal temperature proxy
FFT analysis of search terms alone yields a lot of noise, vague periodicity around 6 month, 1 year marks

13. Conclusions More robust analysis is needed
Specifically, real temperature data, if we want to get anything useful
Preliminary results indicate a possible correlation between seasonal temperature changes and public interest in global climate change
Pizza is way more interesting to the general public than global climate change

14. THANK YOU!
Questions?