A Method for Detecting Social Media Bots by Fusing Multimodal Information and Large Language Models

This project proposes a social media bot detection method that integrates multimodal information with large language models. By combining multi-dimensional data such as text, images, and user behavior, and using the strong comprehension capabilities of large language models, it achieves more accurate identification of account authenticity. This scheme aims to address the declining effectiveness of traditional single-dimensional detection methods and provide technical support for maintaining the health of the social media ecosystem.

Definition and Harm of Bots

Social media bots can simulate human user interactions, but a large number of malicious bots are used for harmful activities such as spreading false information and manipulating public opinion.

Limitations of Traditional Methods

Traditional detection relies on single features (e.g., account metadata, text patterns) and struggles to cope with the evolution of bot technology.

Innovation Direction of This Project

Integrate multimodal information and large language models to conduct comprehensive analysis from dimensions such as text, images, and behavior, thereby improving detection accuracy.

Multimodal Information Fusion

• Text Modality: Language style, comment semantics, username/bio, posting time frequency
• Visual Modality: Avatar authenticity, image content understanding, AI-generated trace detection
• Behavioral Modality: Follower network structure, interaction time patterns, device fingerprints
• Relational Modality: Social graph position, interaction patterns, community affiliation

Core Roles of Large Language Models

• Semantic understanding: Identify text semantic coherence and abnormal emotional expression
• Cross-modal association: Establish matching judgment between avatars and content
• Reasoning ability: Integrate weak signals to form high-confidence judgments
• Few-shot learning: Quickly adapt to new bot patterns

Feature Extraction Layer

• Text encoder: BERT/RoBERTa to convert semantic vectors
• Visual encoder: Vision Transformer/CNN to extract image features
• Behavioral encoder: Time-series behavior encoding
• Graph neural network: Process social relationship graph features

Multimodal Fusion Layer

• Early fusion: Feature-level concatenation/weighting
• Attention mechanism: Dynamically focus on key modalities
• Late fusion: Decision fusion after independent prediction of each modality
• Large model fusion: Convert multimodal information into natural language input for LLM reasoning

Detection Decision Layer

• Binary classification output: Bot/human probability
• Interpretability output: Provide judgment basis
• Confidence estimation: Mark low-confidence samples for manual review

Dataset Construction

• Public datasets: Benchmark datasets such as Twibot-20 and Cresci
• Active sampling: Manually label hard-to-classify samples
• Data augmentation: Synthesize/perturb to expand training data

Evaluation Metrics

• Accuracy, precision, recall, F1-score, AUC-ROC, false positive rate

Comparative Experiments

Compare with traditional ML (Random Forest/SVM), deep learning baselines (LSTM/CNN), graph neural networks, single-modal large models, and multimodal fusion methods

Platform Governance

• Active detection: Real-time evaluation of account registration/content posting
• Batch review: Regular scanning of existing accounts
• Activity monitoring: Strengthen monitoring during elections/major events

Public Opinion Analysis

• Identify information manipulation activities
• Analyze bot network structure and propagation patterns
• Evaluate the authenticity of the public opinion field

Security Protection

• Identify fake brand accounts
• Detect impersonation/phishing accounts
• Protect users from fraud

Adversarial Attacks

• Challenge: Bots evade detection
• Solution: Adversarial training, focus on behavioral patterns

Class Imbalance

• Challenge: Real accounts are far more than bots
• Solution: Oversampling/undersampling, cost-sensitive learning

Concept Drift

• Challenge: Bot behavior evolves over time
• Solution: Online learning, long-term behavioral pattern analysis

Privacy Protection

• Challenge: User data privacy issues
• Solution: Federated learning, differential privacy

Summary

This scheme integrates multimodal information with LLM, breaks through the bottleneck of traditional detection, improves accuracy and adaptability, and provides technical support for maintaining the network ecosystem.

Future Directions

• Technical Evolution: Introduce video/audio modalities, efficient architectures, real-time detection optimization
• Application Expansion: Expand to multiple platforms, develop APIs, bot detection as a service
• Ethical Considerations: Fairness research, prevent unintended harm (e.g., misclassifying real users), transparent appeal mechanism