Fig 1.
The daily raw frequencies (top) and relative frequencies (bottom) of the word “climate” on Twitter from September 14, 2008 to July 14, 2014.
The insets (in red) show the same quantity with a logarithmically spaced y-axis.
Fig 2.
Average happiness of tweets containing the word “climate” from September 2008 to July 2014 by day (top), by week (middle), and by month (bottom).
The average happiness of all tweets during the same time period is shown with a dotted red line. Several of the happiest and saddest dates are indicated on each plot, and are explored in subsequent figures.
Fig 3.
A word shift graph comparing the happiness of tweets containing the word “climate” to unfiltered tweets.
The reference text is roughly 100 billion tweets from September 2008 to July 2014. The comparison text is tweets containing the word “climate” from September 2008 to July 2014. A yellow bar indicates a word with an above average happiness score. A purple bar indicates a word with below average happiness score. A down arrow indicates that this word is used less within tweets containing the word “climate”. An up arrow indicates that this word is used more within tweets containing the word “climate”. Words on the left side of the graph are contributing to making the comparison text (climate tweets) less happy. Words on the right side of the graph are contributing to making the comparison text more happy. The small plot in the lower left corner shows how the individual words contribute to the total shift in happiness. The gray squares in the lower right corner compare the sizes of the two texts, roughly 107 vs 1012 words. The circles in the lower right corner indicate how many happy words were used more or less and how many sad words were used more or less in the comparison text.
Fig 4.
Word shift graphs for three of the happiest days in the climate tweet time series.
Fig 5.
Example tweets on the happiest and saddest days for climate conversation on Twitter.
Fig 6.
Word shift graphs for 3 of the saddest days in the climate tweet time series.
Fig 7.
Frequency of the word “hurricane” (top) and “tornado” (bottom) within tweets containing the word “climate”.
Several spikes have been identified with the hurricane or tornado that took place during that time period.
Fig 8.
Decay rates of the words “hurricane” (top) and “climate” (bottom).
The left plots gives the time series of each word during hurricane Sandy. The right plots gives the power law fit for the decay in relative frequency, x-axes are spaced logarithmically. The power law exponents are given in the titles of the figures.
Fig 9.
Happiness time series plots for tweets containing the word “climate” one week before and one week after three natural disasters in the United States (top) and word shift graphs indicating what words contributed most to the drop in happiness during the natural disasters (bottom).
The word shift graphs compare the climate tweets to unfiltered tweets on the day of the natural disaster.
Fig 10.
Left: Happiness time series plot for unfiltered tweets (red dashed) and tweets containing the word “climate” (blue solid) one week before and one week after the Forward on Climate Rally. Right: word shift plot for climate tweets versus unfiltered tweets on the day of the rally.