The problem is similar to forecasting new products by analogy, where the magnitudes of the values in time series may differ, but what you want to compare is the overall shape. Looking at some of that literature could be useful.
A very basic approach, along those lines--:
If you have a few different time series that you want to use to model another one, you could choose a time window (e.g. 8 weeks after the event in question) and set the value of each observation to equal the percentage of the total value over the entire window. For example, if after a particular celebrity's death, the first week contains 40% of the activity seen over the entire 8 week period, that would be your first observation; then fill in the percentages for the remaining weeks in the period.
If you do that for all of the relevant time series, you can then compare the overall shape of each; you can disregard any you think are outliers, then apply a simple or weighted average to each period to get an aggregate shape. That can then be used to build a model for the time series you want to forecast.
The process I'm describing is along the lines of thisoutlined in this SAS blog post, which may be useful for you-- http://blogs.sas.com/content/forecasting/2014/01/10/forecasting-new-products-part-3-by-structured-analogy/ to read as well.
I realize I'm not providing any coding help here, but hopefully it's at least a start to how you might approach the problem conceptually.
