{"total":35,"rows":[{"id":"2106.04714","title":"NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs","created":"2021-06-08","cited_by_count":73,"area":null},{"id":"2206.04564","title":"TwiBot-22: Towards Graph-Based Twitter Bot Detection","created":"2022-06-09","cited_by_count":44,"area":null},{"id":"2307.16336","title":"Anatomy of an AI-powered malicious social botnet","created":"2023-07-30","cited_by_count":36,"area":null},{"id":"2402.00371","title":"What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection","created":"2024-02-01","cited_by_count":25,"area":null},{"id":"2306.17408","title":"LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection","created":"2023-06-30","cited_by_count":17,"area":null},{"id":"2208.08320","title":"BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency","created":"2022-08-17","cited_by_count":14,"area":null},{"id":"2301.07015","title":"Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection","created":"2023-01-17","cited_by_count":13,"area":null},{"id":"2210.08786","title":"Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls","created":"2022-10-17","cited_by_count":8,"area":null},{"id":"2203.05966","title":"Online User Profiling to Detect Social Bots on Twitter","created":"2022-03-09","cited_by_count":7,"area":null},{"id":"2109.07909","title":"Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language","created":"2021-09-13","cited_by_count":6,"area":null},{"id":"2101.09345","title":"BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets","created":"2021-01-22","cited_by_count":4,"area":null},{"id":"2209.10361","title":"MulBot: Unsupervised Bot Detection Based on Multivariate Time Series","created":"2022-09-21","cited_by_count":3,"area":null},{"id":"2308.12497","title":"False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations","created":"2023-08-24","cited_by_count":3,"area":null},{"id":"2304.07535","title":"From Online Behaviours to Images: A Novel Approach to Social Bot Detection","created":"2023-04-15","cited_by_count":2,"area":null},{"id":"2308.05353","title":"Preemptive Detection of Fake Accounts on Social Networks via Multi-Class Preferential Attachment Classifiers","created":"2023-08-10","cited_by_count":2,"area":null},{"id":"2308.14484","title":"Multimodal Detection of Bots on X (Twitter) using Transformers","created":"2023-08-28","cited_by_count":2,"area":null},{"id":"2406.12570","title":"Applying Ensemble Methods to Model-Agnostic Machine-Generated Text Detection","created":"2024-06-18","cited_by_count":2,"area":null},{"id":"2204.07436","title":"Political Communities on Twitter: Case Study of the 2022 French Presidential Election","created":"2022-04-15","cited_by_count":1,"area":null},{"id":"2411.16285","title":"A Graph Neural Architecture Search Approach for Identifying Bots in Social Media","created":"2024-11-25","cited_by_count":1,"area":null},{"id":"2512.16103","title":"AIMM: An AI-Driven Multimodal Framework for Detecting Social-Media-Influenced Stock Market Manipulation","created":"2025-12-18","cited_by_count":1,"area":null},{"id":"2602.00318","title":"Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2104.03428","title":"Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced Problem","created":"2021-04-07","cited_by_count":0,"area":null},{"id":"2109.03710","title":"BotSpot: Deep Learning Classification of Bot Accounts within Twitter","created":"2021-09-08","cited_by_count":0,"area":null},{"id":"2306.00037","title":"BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline","created":"2023-05-31","cited_by_count":0,"area":null},{"id":"2311.05079","title":"Social Media Bot Detection using Dropout-GAN","created":"2023-11-09","cited_by_count":0,"area":null},{"id":"2402.17392","title":"Spot the bot: Coarse-Grained Partition of Semantic Paths for Bots and Humans","created":"2024-02-27","cited_by_count":0,"area":null},{"id":"2405.02016","title":"Adversarial Botometer: Adversarial Analysis for Social Bot Detection","created":"2024-05-03","cited_by_count":0,"area":null},{"id":"2409.02965","title":"Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations","created":"2024-09-04","cited_by_count":0,"area":null},{"id":"2411.02174","title":"Behavioral Sequence Modeling with Ensemble Learning","created":"2024-11-04","cited_by_count":0,"area":null},{"id":"2503.03775","title":"BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference","created":"2025-03-04","cited_by_count":0,"area":null},{"id":"2510.23648","title":"RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection","created":"2025-10-25","cited_by_count":0,"area":null},{"id":"2511.08455","title":"Bot Meets Shortcut: How Can LLMs Aid in Handling Unknown Invariance OOD Scenarios?","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2605.06435","title":"COVID-19 Infodemic. Understanding content features in detecting fake news using a machine learning approach","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.27584","title":"Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2605.30166","title":"SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection","created":"2026-05-28","cited_by_count":0,"area":null}],"next_cursor":null}