Depending on how complex you want to model the dynamic, if you are restricting to using Prophet (i.e. regression like model), it means you need to fit a model with corresponding predictor so you can do posterior prediction conditioned on different parameter set. More specifically, mapping the question you have into a predictor (i.e., a column of value that has the same length as the number of time step):
“how the number of posts will look like in the long run if users of the site become more active in comments”
→ a predictor of current comment. You can fit another time series of the number of comments over time (probably better to have number of comments per post). Since delay effect is likely, you can use the average of the last week or last month.
Once you create this predictor, plug into Prophet as a new predictor of your time series model. After fitting, you can see the impact of this predictor by looking at the coefficient (just 1 scalar number), and you can increase or decrease the number to investigate what-if scenarios.
You can do the same for score. For “a group of users who start downvoting / upvoting all posts” you can do something like “percentage of post downvote versus post view per user”?
Outside of regression like model, you can do more fancy thing like agent base simulation (model each user). You can actually consult some of the covid simulation and SIR model for that.