Over the last few years, the adoption of encryption in network traffic has been constantly increasing. The percentage of encrypted communications worldwide is estimated to exceed 90%. Although network encryption protocols mainly aim to secure and protect users' online activities and communications, they have been exploited by malicious entities that hide their presence in the network. It was estimated that in 2022, more than 85% of the malware used encrypted communication channels.

On 24 February 2022, Russia invaded Ukraine, starting what is now known as the Russo-Ukrainian War, initiating an online discourse on SNs. Twitter 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 violations, and consequently to user accounts' suspension and deletion. This study focuses on the Twitter suspension mechanism and the analysis of shared content and features leading to an accurate machine-learning suspension prediction. 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 ML methodology including a SHapley Additive explainability model to understand and explain how user accounts get suspended.