Zero inflated negative binomial regression interpretation of categorical variable stata I am using a zero-inflated negative binomial regression model for my data analysis, where the dependent variable is the number of retweets. I have three categorical variables called emotionally_charged (0 if not 1 if yes), hashtag_used (0 if not 1 if yes), and media_used (0 if not 1 if yes). The base reference for all three categorical variables is 1. I need to understand how to interpret the effect of all three on the number of retweets (a count variable) according to the signs of coefficients. Following are my partial results:
VARIABLE            |    Coefficient |  p-value
1.emotionally_charged  | -0.5825642   |  0.000
1.hashtag_used       |   -0.6320729    | 0.000
1.media_used         |    0.1608499  |   0.206
 A: In both Poisson and negative binomial regression, if you exponentiate the raw coefficients, you get an incidence rate ratio. That is not being emotionally charged (if I read your post correctly, even though it seems to contradict the output) is associated with an IRR of $e^{-0.5825642} = 0.558$. You need to note that this is the IRR conditional on not being a structural zero.
That is, a zero-inflated model estimates the probability of always responding a zero, and the coefficients of a count model conditional on not being a zero. You didn't show the zero-inflated part of the model, but those coefficients are for the log-odds of being a structural zero. You interpret those just like a logistic regression.
A zero-inflated model output is not that complicated to interpret. However, you should ask yourself a) is a zero-inflated model plausible, and b) even if a is true, is a zero-inflated model a better model for the data than a normal count model? That is, are there some people in the sample who will never retweet? This seems like a justifiable assumption, but it isn't always. Even if so, say the marginal distribution of the outcome has a lot of zeros. That could be adequately explained by a very low rate.
