  On 24 February 2022, Russia invaded Ukraine, starting what is now known as
the Russo-Ukrainian War, initiating an online discourse on social media.
Twitter as one of the most popular SNs, with an open and democratic character,
enables a transparent discussion among its large user base. Unfortunately, this
often leads to Twitter's policy violations, propaganda, abusive actions, civil
integrity violation, and consequently to user accounts' suspension and
deletion. This study focuses on the Twitter suspension mechanism and the
analysis of shared content and features of the user accounts that may lead to
this. Toward this goal, we have obtained a dataset containing 107.7M tweets,
originating from 9.8 million users, using Twitter API. We extract the
categories of shared content of the suspended accounts and explain their
characteristics, through the extraction of text embeddings in junction with
cosine similarity clustering. Our results reveal scam campaigns taking
advantage of trending topics regarding the Russia-Ukrainian conflict for
Bitcoin and Ethereum fraud, spam, and advertisement campaigns. Additionally, we
apply a machine learning methodology including a SHapley Additive
explainability model to understand and explain how user accounts get suspended.
