Russo-Ukrainian War: Prediction and explanation of Twitter suspension

• URL: http://arxiv.org/abs/2306.03502v2
• Date: Wed, 27 Dec 2023 11:51:15 GMT
• Title: Russo-Ukrainian War: Prediction and explanation of Twitter suspension
• Authors: Alexander Shevtsov, Despoina Antonakaki, Ioannis Lamprou, Ioannis Kontogiorgakis, Polyvios Pratikakis, Sotiris Ioannidis
• Abstract summary: This study focuses on the Twitter suspension mechanism and the analysis of shared content and features of user accounts that may lead to this. We have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API. Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin fraud, spam, and advertisement campaigns.
• Score: 47.61306219245444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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