Russo-Ukrainian War: Prediction and explanation of Twitter suspension
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 57 references that are in the map. First posted on arXiv 6 Jun 2023.
Builds on
22 of 57 references.
References · 22
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
- 2020Language-agnostic BERT Sentence Embeddingcited by 627
- 2018Large Scale Crowdsourcing and Characterization of Twitter Abusive Behaviorcited by 270
- 2019PyTorch-BigGraph: A Large-scale Graph Embedding Systemcited by 119
- 2021TwiBot-20: A Comprehensive Twitter Bot Detection Benchmarkcited by 51
- 2017The paradigm-shift of social spambots: Evidence, theories, and tools for the arms racecited by 49
- 2016BotOrNot: A System to Evaluate Social Botscited by 41
- 2021Heterogeneity-aware Twitter Bot Detection with Relational Graph Transformerscited by 41
- 2017Mean Birds: Detecting Aggression and Bullying on Twittercited by 29
- 2016Multilingual Twitter Sentiment Classification: The Role of Human Annotatorscited by 24
- 2018Disinformation Warfare: Understanding State-Sponsored Trolls on Twitter and Their Influence on the Webcited by 18
10 more
- 2016The DARPA Twitter Bot Challengecited by 15
- 2021PyTorch-Direct: Enabling GPU Centric Data Access for Very Large Graph Neural Network Training with Irregular Accessescited by 12
- 2019Still out there: Modeling and Identifying Russian Troll Accounts on Twittercited by 11
- 2013PhishAri: Automatic Realtime Phishing Detection on Twittercited by 4
- 2018A Web Scraping Methodology for Bypassing Twitter API Restrictionscited by 3
- 2021Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Studycited by 3
- 2020Discovery and classification of Twitter botscited by 2
- 2021Examining Factors Associated with Twitter Account Suspension Following the 2020 U.S. Presidential Electioncited by 2
- 2018twAwler: A lightweight twitter crawlercited by 1
- 2021I'll be back: Examining Restored Accounts On Twittercited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.