A Data-Driven Multimodal Method for Early Detection of Coordinated Abnormal Behaviors in Live-Streaming Platforms

arXiv:2609.01649 · cs.SI, cs.AI · Submitted 2026-08-30 · Read on arXiv

cs.SI, cs.AI

Submitted: 2026-08-30

Updated: 2026-08-30

Terminology

• Alignment — 47× in this paper · explained in 1 episode
• Generalization — 24× in this paper · explained in 13 episodes
• Transformer — 22× in this paper · explained in 2 episodes
• AUC — 19× in this paper · explained in 2 episodes
• Consistency — 14× in this paper · explained in 3 episodes
• Core — 12× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Accuracy — 8× in this paper · explained in 1 episode
• Multimodal Fusion — 8× in this paper · explained in 3 episodes
• Representation Learning — 8× in this paper · explained in 2 episodes
• Attention Mechanism — 5× in this paper · explained in 3 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Early Detection Score — 5× in this paper
• Robustness — 5× in this paper · explained in 5 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Distribution Shift — 4× in this paper · explained in 6 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Self-Supervised Learning — 4× in this paper · explained in 8 episodes
• Transferability — 4× in this paper · explained in 4 episodes
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Cooperation — 3× in this paper · explained in 2 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• Feature Engineering — 3× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Cross-Domain Generalization Score — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Fraud Detection Rate — 2× in this paper
• Graph Construction — 2× in this paper · explained in 3 episodes
• Heterogeneous Graph — 2× in this paper · explained in 2 episodes
• Human-in-the-Loop — 2× in this paper · explained in 3 episodes
• Redundancy — 2× in this paper · explained in 2 episodes
• Transformer Models — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Analyzer — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Dynamic Modeling — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• You Can Use But Cannot Recognize: Preserving Visual Privacy in Deep Neural Networks
• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
• Supervised Multimodal Bitransformers for Classifying Images and Text

Related papers

• Linking Scalar-Intensity Language to Structural Polarization with Validated Signed-Network Measures
• Detection and Characterization of Coordinated Online Behavior: A Survey
• Omega-N: Interpretable Structural Node Descriptors and Their Applicability Domain
• Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection
• Transmission Neural Networks: Inhibitory and Excitatory Connections
• A family of graph GOSPA metrics for graphs with different sizes