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Table 6 gives the formula that we used to construct the regression model with the glmer() function in the R package lme4 (Bates et al., 2014).
self_promotion ~
gender	// M, F
+ party	// D, R
+ chamber	// house, senate
+ age	// numerical
+ num_terms	// numerical
+ dailytweets	// numerical
+ followers_log	// numerical
+(1 | tweet_author)	// random effect
+(1 | tweet_date)	// random effect
Table 6: Formula of the logistic linear mixed-effects regression model.
Appendix C
Figure 3: Odds ratios of the above independent variables obtained by fitting the regression model with 2 million Congressional tweets. For age, here we present its odds ratio in terms of  $age / 10$  (without scaling, the ratio is 0.99).
Figure 4: Distribution of the four numerical factors used in the regression model: age, num_terms, dailytweets, and followers_log. Our data span about 4 years, so the value of age or num_terms changes as the tweet date varies; as a result, we show each person's average values (in terms of median). Similarly, for variable dailytweets, we also show a person's average here.