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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

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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.

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