Enterprise AI Analysis
A Large-Scale Dataset of Interactions Between Weibo Users and Platform-Empowered LLM Agent
Discover actionable insights from cutting-edge research to transform your enterprise operations.
Unveiling Human-LLM Dynamics on Weibo
This research introduces the CommentR Interaction Dataset, a comprehensive, real-world dataset capturing interactions between human users and the LLM-powered agent CommentRobert on Weibo. With over 557,000 interactions from 304,400 unique users over 17 months, this dataset provides a unique lens to understand how humans perceive, trust, and communicate with LLMs in natural social settings. Our analysis reveals distinctive patterns in user demographics, interaction dynamics, and linguistic features, offering critical insights for designing safer and more engaging AI agents. The dataset is publicly available, fostering further research into human-AI collaboration.
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Total Interactions Captured
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Unique Users Engaged
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Observation Period
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Female User Ratio
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Understanding who interacts with LLM agents is crucial for targeted design. The dataset reveals significant demographic trends.
81.1%
of CommentR users are Female, suggesting a particular appeal to this demographic.
Mostly Unverified
accounts interact, indicating that ordinary users, not influencers, drive engagement.
| Characteristic | Human Users (General Weibo) | CommentR Engaged Users |
|---|---|---|
| Median Followees | High (>1000) | 362 |
| Popularity | Influencer/Org | Ordinary Users |
| Interaction Frequency | Varied | Predominantly Low |
The timing of interactions sheds light on user motivations and the role LLMs play in daily routines.
Late Night Peak
Over 30% of high-intensity interactions occur between 10 PM and midnight.
Interaction Activity Flow
Morning/Daytime: Low Activity
→
Evening: Rising Engagement
→
Late Night: Peak Conversation
→
Midnight: Sustained Interaction
Case Study: Late-Night Companionship
Analysis of late-night interactions reveals a shift from functional queries to emotional companionship. Users often treat CommentR as a 'social partner' or 'emotional outlet' during these hours, demonstrating a deeper, more personal engagement with the LLM agent. This highlights the potential for AI to fill social voids and provide support outside traditional hours.
Conversation topics change significantly with increased interaction frequency, indicating evolving user expectations.
| Frequency Tier | Primary Themes | User Perception of LLM |
|---|---|---|
| Low (1-99 mentions) | Functional queries, summaries, platform tools | Virtual Assistant / Tool |
| Medium (100-999 mentions) | Entertainment, fandom discussions, information acquisition | Community Participant |
| High (1000+ mentions) | Emotional sharing, nicknames, personal feelings | Social Partner / Emotional Outlet |
Case Study: From Tool to Friend
Users who interact with CommentR frequently develop a more personal relationship, often using nicknames like '萝卜头' (carrot head) and expressing a wide range of emotions. This evolution from seeing the LLM as a mere tool to a trusted emotional companion underscores the profound impact of anthropomorphic AI design and long-term interaction.
Distinct linguistic characteristics differentiate LLM-generated comments from human ones, offering insights for detection and design.
| Feature | CommentR (LLM) | Human Users |
|---|---|---|
| Comment Length | Slightly longer (10-20 words peak), occasional long paragraphs (150-300 words) | Very short (1-10 words peak), rarely exceeding 150 words |
| Emoji Usage | Highly conservative (0-2 emojis), over 90% with none or 1-2 | Frequent and varied (1-10+ emojis) |
| Lexical Style (SCLIWC) | More narrative, uses Relative/Time words, emphasizes description/comparison/insight | More institutional, polite, frequent Negate/Exclude words, more emotionally expressive (both positive and negative) |
Fewer Emojis
CommentR uses significantly fewer emojis, offering a clear differentiator for AI-generated content detection.
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Your Path to AI-Powered Engagement
A phased approach to integrating LLM agents into your social media strategy, ensuring seamless adoption and measurable success.
Phase 1: Discovery & Strategy
Assess current engagement challenges, define LLM agent objectives, and tailor a strategy for platform integration and user interaction. This phase includes data analysis and initial use case identification.
Phase 2: Pilot Deployment & Training
Implement a pilot LLM agent with a limited user group. Gather feedback, fine-tune conversational capabilities, and train the agent on brand voice and specific interaction protocols. Focus on early user experience.
Phase 3: Full-Scale Rollout & Monitoring
Deploy the LLM agent across your target audience. Continuously monitor performance metrics, user sentiment, and interaction patterns. Iterate on agent behavior and content generation based on real-world data.
Phase 4: Optimization & Expansion
Refine agent capabilities for advanced tasks, explore cross-platform integration, and identify new opportunities for LLM-powered engagement to drive long-term value and deepen user relationships.
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