Weibo Personality Analyzer

Fri, 09 May 2025 00:00:00 +0000

Background

Time: May 2026
Context: CS114 course project.
Project title: 微博性格分析师：利用生成式AI洞察社交媒体人格
English title: Weibo Personality Analyzer: Using Generative AI to Gain Insight into Social Media Persona

Goal

The project aims to build a web-based tool called “Weibo Personality Analyzer”. By collecting and analyzing a Weibo user’s public posts, the system infers content themes, writing style, and personality-related tendencies from social media text.

The core role of AI is to use the Qwen large language model to process unstructured Weibo posts, extract linguistic signals, and evaluate whether generative AI can support social media persona analysis in a structured and explainable workflow.

System Workflow

User enters a target Weibo username through the web interface at callitwhatyouwant.cn(now unavailable).
The backend launches a Selenium-based scraper through scraper.py, which simulates browser behavior for more stable data collection.
The system supports collecting around 50 to 300 public posts.
Raw posts are cleaned and converted into a structured Markdown file.
Qwen analyzes the Markdown input and produces a Markdown report.
The Flask backend formats the result and returns it to the web frontend.

AI Analysis

The Qwen LLM is used as the core analysis engine. It receives structured Markdown content and produces analysis across three main dimensions:

Content theme modeling: clusters recurring topics and interest areas.
Writing style analysis: evaluates formality, casualness, emotional tendency, and expression habits.
Personality inference: maps language features to MBTI-inspired dimensions while avoiding unsupported psychological diagnosis.

Web Application

Frontend: HTML, CSS, and JavaScript with responsive design.
Backend: Python Flask routes for request handling, scraper triggering, Qwen API calls, and result formatting.
Deployment: Tencent Cloud ECS server with the domain callitwhatyouwant.cn.

Prompt Engineering

The prompt design went through several iterations:

Version	Prompt Strategy	Result
V1.0	“Analyze these Weibo posts: [text]”	Too general and lacked depth.
V2.0	Asked the model to act as a social media analyst and output theme, style, and personality sections.	Structure improved, but personality analysis was still weak.
V3.0	Added MBTI-based dimensions, negative constraints against unsupported mental-state claims, and required Markdown output.	Reliability and readability improved significantly.

Example prompt direction:

As a social media analyst, analyze the following public Weibo posts from three perspectives: content themes, writing style, and MBTI-inspired personality tendencies. Do not infer private mental health status or make claims unsupported by the text. Output the result in Markdown.

Role of Generative AI

Analysis engine: Qwen performs multi-dimensional text interpretation, including topic, sentiment, style, and persona-related signals.
Development accelerator: generative AI helped draft frontend structure, CSS styling, Flask route templates, and debugging suggestions.
Debugging support: AI-assisted log interpretation helped locate syntax and logic errors during development.
Security suggestions: AI provided reminders such as cookie encryption and safer handling of user data.

Tech Stack

Qwen LLM
Python
Selenium
Flask
HTML/CSS
JavaScript
Markdown parsing
Tencent Cloud ECS

Tools Used

Qwen large language model for post analysis.
Claude 3.7 Sonnet for backend development support.
Bolt.new for frontend design support.

Social Media Analysis on Zitao Liao