Enterprise AI Analysis
AIGC Research on Content Innovation and Enhancing User Engagement in Social Media Marketing
Authors: Tao Zhou, Xinxing Luo, Xinyu Yang, Sheng Cao
Publication: 2025 6th International Conference on Computer Information and Big Data Applications (CIBDA 2025)
Executive Impact: Transforming Social Media Marketing
This research demonstrates the significant positive effects of AIGC technology on social media marketing, particularly in enhancing content innovation and user engagement. Through a validated model, it shows how immersion experience, self-efficacy, and community influence drive persistent user behavior. The implementation of a GAN-BERT model for intelligent conversation achieves a BLEU value of 44.25, significantly improving dialogue naturalness and accuracy. AIGC effectively reduces content creation costs and boosts efficiency by automating high-quality text, pictures, and video. The findings provide critical theoretical support and practical guidance for e-commerce platforms.
44.25
GAN-BERT BLEU Score
0.42
Immersion Experience Impact (Path Coeff.)
0.38
Self-Efficacy Impact (Path Coeff.)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AIGC Technology Overview
User Behavior Analysis
System Design & Integration
Impact & Future Directions
AIGC (Generative Artificial Intelligence) technology leverages deep learning and natural language processing (NLP) to automatically generate high-quality text, pictures, and videos. This significantly reduces content creation costs and enhances production efficiency. The paper highlights the powerful capabilities of models like GANs and BERT, including the ChatGPT large model, for natural language generation and intelligent conversation. While promising, challenges such as content quality control, emotional expression accuracy, and algorithm optimization remain.
Based on information ecology theory, a model was developed and validated through a questionnaire survey of 298 AIGC platform users. The research found significant positive effects of immersion experience (path coefficient 0.42, p<0.001), self-efficacy (path coefficient 0.38, p<0.001), and community influence (indirect effect 0.25, p<0.01) on users' persistent use behavior. The model demonstrates robust reliability and validity (e.g., Cronbach's alpha >0.80, GFI=0.912, RMSEA=0.046), suggesting that enhancing these factors can improve user stickiness on AIGC platforms.
An innovative e-commerce system was designed, integrating K-means, AIGC, and NLP technologies. K-means clustering generates accurate user portraits based on purchasing power and preferences. The Stable Diffusion model creates personalized virtual anchor images, while automatic speech synthesis (TTS) and action generation provide natural and dynamic performance. The GAN-BERT model enables intelligent conversation, optimizing natural language understanding and dialogue generation, leading to a BLEU score of 44.25. This integration aims to provide personalized services and enhance user interaction.
AIGC technology effectively reduces content creation costs for merchants and improves efficiency through automated generation of high-quality content. It also enhances user interaction and engagement by offering personalized experiences and intelligent dialogues. Future work includes optimizing algorithms, integrating larger models like ChatGPT to further enhance virtual shopping guide interactions, and exploring multi-modal content generation and real-time sentiment analysis to improve overall user experience and system intelligence.
44.25
BLEU Score Achieved by GAN-BERT for dialogue naturalness and accuracy.
Enterprise Process Flow
User Behavior Analysis (K-means)
→
Personalized Content Generation (Stable Diffusion)
→
Intelligent Interaction (GAN-BERT)
→
Optimized User Experience & Brand Loyalty
| Feature | GAN-BERT Model | Traditional Models |
|---|---|---|
| Dialogue Naturalness | Significantly improved, BLEU 44.25 | Lower naturalness and accuracy |
| Accuracy of Dialogue | High accuracy, enhanced understanding | Faces challenges in nuanced understanding |
| Content Generation Cost | Effectively reduced | Higher cost and lower efficiency |
| Efficiency of Creation | Significantly improved | Lower efficiency, manual efforts |
E-commerce System Implementation
The research details the implementation of an e-commerce system integrating AIGC and NLP to optimize user experience. Key components include:
- User Portrait Module: Utilizes K-means clustering for accurate user segmentation based on purchasing power and preferences.
- Virtual Anchor Module: Employs Stable Diffusion for generating personalized virtual anchors and TTS technology for natural speech and dynamic performance.
- Intelligent Text Dialogue Module: Leverages GAN-BERT for advanced natural language understanding and generation, facilitating intelligent conversations with users.
- Personalized Recommendations: Continuously optimizes product and information recommendations based on user feedback to enhance interaction effects and shopping experience.
This system demonstrates how AIGC can deliver personalized services, simplify decision-making, and improve overall user satisfaction in social media marketing.
Projected ROI with AIGC Integration
Estimate the potential time and cost savings for your enterprise by integrating AIGC solutions into content generation and user engagement workflows.
Projected Annual Savings
$0
Annual Hours Reclaimed
0
Implementation Roadmap: Integrating AIGC
Our phased approach ensures a smooth transition and maximum impact for your social media marketing and content strategy.
Phase 1: Discovery & Strategy Alignment
Conduct a comprehensive analysis of current content creation workflows, identify key pain points, and define strategic objectives for AIGC integration. This includes user behavior deep dives and initial model customization planning.
Phase 2: Core AIGC System Development
Implement K-means for user profiling, integrate Stable Diffusion for virtual anchor generation, and deploy GAN-BERT for intelligent dialogue capabilities within a scalable microservice architecture.
Phase 3: Pilot Deployment & Iterative Optimization
Launch AIGC features in a pilot environment, collect user feedback, and continuously refine algorithms for content recommendation, dialogue accuracy, and virtual anchor performance. This phase focuses on achieving the BLEU score and user engagement targets.
Phase 4: Full-Scale Integration & Advanced Features
Roll out AIGC across all relevant social media marketing channels. Explore advanced capabilities such as multi-modal content generation, real-time sentiment analysis, and integration with larger language models for enhanced interactive experiences.
Ready to Transform Your Social Media Marketing with AIGC?
Book a personalized consultation with our AI strategists to explore how these insights can be tailored to your business needs and drive unparalleled engagement.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat