  Online social networks like Twitter actively monitor their platform to
identify accounts that go against their rules. Twitter enforces account level
moderation, i.e. suspension of a Twitter account in severe cases of platform
abuse. A point of note is that these suspensions are sometimes temporary and
even incorrect. Twitter provides a redressal mechanism to 'restore' suspended
accounts. We refer to all suspended accounts who later have their suspension
reversed as 'restored accounts'. In this paper, we release the firstever
dataset and methodology 1 to identify restored accounts. We inspect account
properties and tweets of these restored accounts to get key insights into the
effects of suspension.We build a prediction model to classify an account into
normal, suspended or restored. We use SHAP values to interpret this model and
identify important features. SHAP (SHapley Additive exPlanations) is a method
to explain individual predictions. We show that profile features like date of
account creation and the ratio of retweets to total tweets are more important
than content-based features like sentiment scores and Ekman emotion scores when
it comes to classification of an account as normal, suspended or restored. We
investigate restored accounts further in the pre-suspension and
post-restoration phases. We see that the number of tweets per account drop by
53.95% in the post-restoration phase, signifying less 'spammy' behaviour after
reversal of suspension. However, there was no substantial difference in the
content of the tweets posted in the pre-suspension and post-restoration phases.
