# Identifying impact of an Interrupted Time-Series Experiments with Twitter's users

PROBLEM: We want to test the impact of an intervention from a public institution on Twitter. The intervention during a week-period of posting certain tweets promoting no toxic language in social media.

DATA: Our data contains the timeline (ten days before and after the first retweet of a tweet from this intervention) of all users that retweeted one of the tweets posted by the public institution. So far we classified the words that their tweets contained in 70 different categories. Besides, we collected general conversation on twitter in case we need to cancel out seasonal trends like san Valentine's day or something like this.

WANT TO KNOW: We need to identify those users that changed the content in their tweets after the intervention date. Firstly I run a t-test to test differences between the mean value of each category after and before the intervention. However, this approach seems to me very rudimental and losing information. I would like to consider this problem as an interrupted time series experiment design.

I am not an expert in time series. So far, I consider applying Causal Impact R package by each user whether or not there were changes in the way they tweet.

Any help would be appreciated!

• Do not know if I understand what you did exactly. The key here is not to use a univariant t-test, but rather consider the information in all 70 categories at once, i.e. exploit the multivariate nature of the problem. If you were finally to perform a test of equality of means, a Hotellings' $T^2$ test rather than a bunch a univariate $t$-tests would be preferable (assuming in both cases the required distributional assumptions hold). Nov 20 '19 at 11:24