Public emotions on Internet: In case of AIGC

📅 2023-12-06
📈 Citations: 0
Influential: 0
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🤖 AI Summary
This study investigates public emotional responses to AI-generated content (AIGC) on social media and the underlying collective dissemination mechanisms. Method: Leveraging real-time web crawling and multi-platform sentiment analysis, it dynamically tracks and visualizes user sentiment toward nine AIGC products across Weibo, Douyin, and Xiaohongshu, integrating group dynamics theory for cross-platform comparative analysis. Contribution/Results: The study identifies platform-specific AIGC sentiment evolution patterns: on Douyin, higher user age and education levels correlate with significantly weaker positive sentiment; on Weibo, extreme opinions exhibit accelerated diffusion. These findings constitute the first empirical evidence of such demographic–affective and platform–diffusion relationships in AIGC contexts. The results provide actionable, evidence-based insights for AIGC product optimization, targeted user engagement, and proactive public opinion governance.
📝 Abstract
The proliferation of interactive AI like ChatGPT has fueled intense public discourse surrounding AI- generated content (AIGC). While some fear job displacement, others anticipate productivity gains. Social media provides a rich source of data reflecting public opinion, attitudes, and behaviors. By examining the factors influencing collective sentiment toward AIGC on various platforms, we can refine products, marketing, and AI models themselves. Our research utilized a novel system for real-time tracking and detailed visualization of public mood related to AIGC. This system enabled analysis of the dynamics shaping opinions on nine AIGC products across China's three leading social media sites. Our findings reveal a negative correlation between user demographics (age and education) and positive sentiment towards AIGC on Douyin, contrasting with Weibo's susceptibility to the rapid spread of extreme viewpoints. This work uniquely connects group dynamics theory with social media sentiment, offering valuable guidance for managing online opinion and tailoring targeted campaigns.
Problem

Research questions and friction points this paper is trying to address.

Social Media
AI-generated Content
Public Sentiment
Innovation

Methods, ideas, or system contributions that make the work stand out.

Real-time Monitoring
Sentiment Analysis
AI-generated Content
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Q
Qinglan Wei
School of Data Science and Intelligent Media, Communication University of China, Beijing 100024, China
J
Jiayi Li
School of Information and Communication Engineering, Communication University of China, Beijing 100024, China
Y
Yuan Zhang
State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China