Source-linked AI summary
A Data-Driven Multimodal Method for Early Detection of Coordinated Abnormal Behaviors in Live-Streaming Platforms
Jingwen Luo, Pinrui Zhu, Yiyan Wang, Zilin Xiao, Jingqi Li, Xuebei Kong, Yan Zhan
TL;DR
Abnormal marketing in live-streaming environments is increasingly concealed, coordinated, and distributed across heterogeneous modalities, challenging early detection and platform governance. MM-FGDNet combines multimodal temporal alignment, temporal fraud-pattern modeling, and cooperative manipulation detection, achieving strong reported performance and stable cross-domain generalization. The framework’s scope remains constrained by computational overhead and dependence on sufficiently observable interaction graphs.
Problem
Existing approaches inadequately detect weak cross-modal signals, evolving fraud patterns, and coordinated group manipulation under changing platforms, streamer types, and product categories.
Method
MM-FGDNet aligns video, text, audio, and user behavior temporally, models fraud evolution, and uses graph-based cooperative behavior detection with diffusion and self-supervised strategies.
Results
MM-FGDNet outperforms representative baselines, achieving an AUC of 0.927, F1 of 0.847, precision of 0.861, recall of 0.834, and an Early Detection Score of 0.689.
Takeaways & Limitations
The framework provides a practically valuable solution for proactive detection of coordinated abnormal behavior in live-streaming and digital marketing environments.
Takeaways & Limitations
The framework incurs computational and storage overhead and may be affected when user interaction graphs are sparse or partially unobservable.
Abstract
from arXiv · showhide
With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed and coordinated across heterogeneous modalities, challenging platform governance and early risk identification. We propose MM-FGDNet, a data-driven multimodal framework for detecting abnormal behavior in large-scale live-streaming environments from complementary temporal-evolution and group-structure perspectives. A cross-modal temporal alignment module maps video, text, audio, and user behavior into a unified temporal semantic space. A temporal fraud-pattern module captures the progression from weak early signals to abrupt outbreaks, while a cooperative manipulation module identifies coordinated interactions among organized user groups and automated accounts. Experiments on real-world multi-platform live-streaming e-commerce datasets show that MM-FGDNet outperforms representative baselines, achieving an AUC of 0.927, F1 of 0.847, precision of 0.861, recall of 0.834, and an Early Detection Score of 0.689, while reducing false alarms. Ablation studies validate the contribution of each module, and cross-domain experiments demonstrate stable generalization to new streamers, product categories, and platforms. These results indicate that MM-FGDNet provides an effective and scalable solution for proactive detection of coordinated abnormal behavior in live-streaming systems.
1. Introduction
Live-streaming marketing fraud is increasingly difficult to detect because behaviors span modalities, evolve over time, and involve coordinated users. MM-FGDNet addresses these challenges through multimodal alignment, temporal anomaly modeling, and cooperative behavior detection.
- Motivation: Existing fraud detectors rely on statistical models, manual features, or machine learning approaches that struggle with rapidly evolving live-streaming marketing environments.These methods are more effective when rules and patterns are clear and stable.
- Motivation: Single-modality or static features, weak-signal detection, and distribution shifts limit existing approaches across platforms, streamer types, and product categories.These limitations reduce sensitivity to covert cross-modal fraud patterns and cross-domain adaptability.
- Framework: MM-FGDNet constructs a multimodal dataset and aligns video, text, audio, and user behavior within a unified temporal semantic space.The framework includes synchronization and quality calibration strategies to improve cross-modal consistency and reliability.
- Framework: The temporal anomaly module captures weak early signals and abrupt fraudulent patterns, while graph neural networks identify organized groups such as paid posters and bot clusters.Interpretable abnormal subgraph analysis supports the identification of coordinated manipulation.
- Significance: MM-FGDNet systematically combines temporal alignment, anomaly evolution modeling, and cooperative behavior identification for early risk detection and cross-platform generalization.The paper presents this integrated framework as a practical solution for digital marketing regulation and platform governance.
2. Related Work
Related work develops multimodal temporal, graph-based, and weakly supervised approaches, but existing methods incompletely connect evolving multimodal evidence with coordinated group manipulation. These gaps motivate jointly modeling temporal dynamics, content semantics, and interaction structure.
- Multimodal Temporal Representation Learning: Multimodal temporal learning combines dynamic information from visual, speech, textual, and interaction channels within shared temporal semantic spaces.Single-modality approaches are insufficient for rapidly changing behavioral nuances.
- Multimodal Temporal Representation Learning: Existing multimodal paradigms often focus on individual users or isolated content and lack explicit modeling of coordinated interactions among multiple accounts.This limits their ability to address organized group-level manipulation in real time.
- Graph-Based Relational Structure Modeling: Traditional statistical, time-series, and transformer methods detect individual behavioral deviations and sudden trend changes but struggle with multi-user coordination.Graph-based relational structures are introduced to address this limitation.
- Graph-Based Relational Structure Modeling: Existing graph models commonly treat interactions as static snapshots or omit fine-grained temporal evolution alongside multimodal content.This separation makes it difficult to distinguish organic community growth from synchronized water-army or bot activity.
- Generative and Weakly Supervised Learning Paradigms: Weakly supervised, self-supervised, generative, and few-shot methods reduce dependence on scarce fraud labels or enrich abnormal-pattern representations.Diffusion models are described as a stable approach for generating high-quality temporal samples.
- Generative and Weakly Supervised Learning Paradigms: These data-scarcity methods generally do not explicitly model the dynamic topological evolution of organized group-level manipulation.Standalone few-shot methods may therefore fail to cover diverse and covert fraudulent behaviors.
3. Materials and Method
The method builds a synchronized multimodal dataset and preprocessing pipeline, then combines temporal anomaly modeling with graph-based cooperative manipulation detection. MM-FGDNet aligns heterogeneous signals, amplifies weak fraud evidence, models abrupt patterns, and fuses temporal and structural risk.
- Data Collection: Six months of data from multiple live-commerce and short-video platforms include video, audio, comments, user interactions, transactions, timestamps, and verified normal or abnormal events.The collection covers different time intervals, streamer types, and product categories, with examples including fake-tipping bursts, homogeneous comments, and coordinated interactions.
- Data Preprocessing and Augmentation: Unified timestamps align modalities, while preprocessing and augmentation reduce noise and improve semantic consistency before modeling.Text, video, and behavior sequences are transformed into lower-noise, more balanced representations; diffusion synthesis targets rare coordinated burst sequences.
- Cooperative Manipulation Detection: The graph module models user coordination through interaction edges, prototype consistency, and structural density, then combines graph and temporal evidence with learnable gating.It identifies patterns such as bot clusters and paid commentator groups; the final score fuses aggregated temporal risk, subgraph risk, and consistency discrepancy.
- Cooperative Manipulation Detection: Cosine-based consistency discrepancy is scale-invariant, supporting stable discrimination across streamers and product categories.The subgraph risk increases monotonically with intra-group edge weights, providing an interpretable response to cooperation strength.
4. Results and Discussion
MM-FGDNet is evaluated against temporal, graph-based, multimodal, and unsupervised baselines using chronological splits. Results indicate stronger overall, early-detection, ablation, and cross-domain performance, while higher computational overhead remains a limitation.
- Experimental Setup: Chronological partitioning assigns the first 70% of the timeline to training, the next 10% to validation, and the final 20% to testing.This design prevents future-information leakage and separates parameter learning, model selection, and final evaluation.
- Baseline Comparison: Unsupervised distributional methods underperform on AUC and F1, while recurrent models improve Recall, F1, and EDS but remain limited on long time spans and abrupt anomalies.The passage attributes these differences to isolated-outlier assumptions and recurrent models’ constrained memory and gradient attenuation.
- Baseline Comparison: Temporal Transformers outperform recurrent models in AUC, F1, and EDS, GNNs improve F1 and CDGS, and multimodal fusion improves Precision, F1, and AUC.The gains correspond to global temporal modeling, user-relationship structure, and complementary visual-textual information, respectively.
- Overall Findings: MM-FGDNet outperforms existing baselines in overall performance, early detection capability, and cross-domain generalization.Its joint temporal, semantic, and structural constraints are described as maintaining a stable decision boundary under complex distributions and scenario shifts.
- Ablation Study: Removing temporal fraud modeling most severely degrades EDS, while removing cooperative detection reduces CDGS and removing alignment lowers AUC and F1 while increasing FAR.The ablation findings associate these changes with lost early-signal sensitivity, weaker cross-domain structural constraints, and greater cross-modal noise.
- Cross-Domain Generalization: Cross-domain performance declines for all models, but MM-FGDNet retains advantages across new streamers, products, and platforms, particularly in EDS.The reported explanation combines cross-modal alignment, domain-independent temporal evidence accumulation, and group-level structural information.
- Limitations: MM-FGDNet’s multimodal and group-level modeling increases computational and storage overhead, creating challenges for ultra-large-scale or latency-sensitive deployment.The limitation requires accuracy-efficiency trade-offs in operational settings.
5. Conclusions
MM-FGDNet addresses concealed, coordinated, cross-modal abnormal marketing behavior by modeling temporal evolution and group-level coordination. Its unified architecture combines multimodal alignment, temporal fraud-pattern modeling, and cooperative manipulation detection, with interpretable subgraph analysis supporting platform governance.
- MM-FGDNet addresses concealed, coordinated, and cross-modal abnormal marketing behaviors in live-streaming e-commerce and digital marketing.
- The framework models abnormal behaviors through complementary temporal-evolution and group-level coordination perspectives.
- MM-FGDNet integrates cross-modal temporal alignment, temporal fraud-pattern modeling, and cooperative manipulation detection into one architecture.
- Interpretable abnormal subgraph analysis visualizes high-order interactions and identifies core members of organized groups, helping human reviewers validate alerts.