Fig 1.
Choropleth maps indicating (A) caloric input Cin and (B) caloric output Cout in the contiguous United States (including the District of Columbia) based on 50 million geotagged tweets taken from 2011–2012.
For both maps, darker means higher values as per the color bars on the right. The histograms in Fig 5, S2 and S3 Figs show the specific rankings according to these two variables and also Crat (see Fig 3). The overlaid phrase lemmas are the most dominant contributors to Cin and Cout—almost universally “pizza” and “watching tv or movie”.
Fig 2.
The same choropleth maps for Cin and Cout presented Fig 1 but now with phrases whose increased usage contribute the most to a population’s Cin and Cout differing from the overall averages of these measures.
See the section on Phrase Shifts in Analysis and Results. For example, tweets from Vermont, which was above average for both Cin and Cout for 2011–2012, disproportionately contain “bacon” and “skiing”. Michigan was above average for Cin and below for Cout in 2011–2012, and the most distinguishing phrases are “chocolate candy” and “laying down”. See Fig 5, S2 and S3 Figs for ordered rankings.
Fig 3.
Choropleth for caloric ratio Crat = Cout/Cin.
Fig 4.
Plots for the contiguous US showing the lack of correlation between caloric input Cin and caloric output Cout, demonstrating their separate value as they bear different kinds of information.
The Pearson correlation coefficient is -0.13 and the best line of fit slope is m = -1.64. S1 Fig adds plots of Crat as a function of Cin and Cout.
Fig 5.
Histograms of caloric intake Cin (food), caloric output Cout (activity), and caloric ratio Crat for the states of the contiguous US, all ranked by decreasing Crat.
Bars indicate the difference in the three quantities from the overall average with colors corresponding to those used in Figs 1, 2 and 3. We provide the same set of histograms re-sorted by Cin and Cout in S2 and S3 Figs.
Fig 6.
Phrase shifts showing which food phrases and physical activity phrases have the most influence on Colorado and Mississippi’s top and bottom ranking for caloric ratio, when compared with the average for the contiguous United States.
Note that phrases are lemmas representing phrase categories. Overall, Colorado scores lower on Twitter food calories (257.4 versus 271.7) and higher on physical activity calories (203.5 versus 161.3) than Mississippi. We provide interactive phrase shifts as part of the paper’s Online Appendices at http://compstorylab.org/share/papers/alajajian2015a/ and at http://panometer.org/instruments/lexicocalorimeter. We explain phrase (word) shifts in the main text (see Eqs 5 and 6), and in full depth in [2] and [16] and online at http://hedonometer.org [23].
Fig 7.
Six demographic quantities compared with caloric ratio Crat for the contiguous US.
The inset values are the Spearman correlation coefficient , and the Benjamini-Hochberg q-value. See Table 1 for a full summary of the 37 demographic quantities studied here.
Table 1.
Spearman correlation coefficients, , and Benjamini-Hochberg q-values for caloric input Cin, caloric output Cout, and caloric ratio Crat = Cout/Cin and demographic, data related to food and physical activity, Big Five personality traits [31], health and well-being rankings by state, and socioeconomic status, correlated, ordered from strongest to weakest Spearman correlations with caloric ratio.
The two breaks in the table indicate significance levels of 0.01 and 0.05 for the Benjamini-Hochberg q of Crat, corresponding to the first 24 health and/or well-being quantities and then the next four, numbers 25 to 28. The bottom 9 quantities were not significantly correlated with Crat according to our tests. S1, S2 and S3 Tables present the same analysis for caloric measures including phrases representing liquids, and for the difference Cdiff(α) = αCout − (1 − α)Cin, both without and with liquids included.
Fig 8.
Screenshot of the interactive dashboard for our prototype Lexicocalorimeter site (taken 2015/07/03).
An archived development version can be found as part of our paper’s Online Appendices at http://compstorylab.org/share/papers/alajajian2015a/maps.html, and a full dynamic implementation will be part of our Panometer project at http://panometer.org/instruments/lexicocalorimeter. See https://github.com/andyreagan/lexicocalorimeter-appendix for source code.