Analyzing Jianshu Platform Data with Python Crawling and FineBI Visualization

While Zhihu dominates the Chinese UGC market, other platforms such as Jianshu are often overlooked; this report explores the quality of Jianshu users, high‑fan‑count accounts, and the most‑read articles.

1. Data Acquisition – The data was collected using Python web‑scraping, retrieving up to 900 followers per user and about 1,900 articles per author, resulting in 261,277 user records containing usernames, profile URLs, contract status, follower/following counts, article counts, total word counts, etc.

Analysis of the 1,916 scraped articles shows the top‑liked article received 17,076 likes, while the least liked had 488, indicating that most popular content was captured.

2. BI Analysis – After obtaining the data, the next step is visualization. Although many front‑end chart libraries exist (Highcharts, ECharts, Chart.js, D3.js), they require coding skills and are not truly open‑source. For non‑technical users, a Business Intelligence (BI) tool is preferable.

Internationally, Tableau is a leading BI product, but its real‑time query capabilities, high cost, lack of built‑in data warehouse, and poor support for complex Chinese tables make it unsuitable for the Chinese market.

Instead, the author chose FineBI, a free‑for‑personal‑use, enterprise‑grade BI platform.

Advantages of FineBI

Automatic and flexible data modeling.

Rich visualizations and front‑end analysis features, supporting drill‑down, slicing, and rotating of multidimensional data.

Built‑in ETL for real‑time analysis and fast processing of large datasets.

3. Data Visualization with FineBI – After installing and activating FineBI, the Python‑scraped dataset was imported for analysis.

3.1 Contracted Author Analysis – Among over 260,000 users, only 126 were explicitly marked as “contracted authors,” indicating a strict author selection process.

3.2 Follower Distribution – A pyramid‑style chart shows that only five users have more than 100,000 followers, while the 10‑100 k follower range accounts for the largest proportion (40.38 %).

3.3 24‑Hour Hot Article Timing – The highest number of articles were posted around 11 am, suggesting that many creators publish in the morning rather than late at night.

3.4 Engagement Metrics – Likes and comments correlate strongly with article popularity, as illustrated by the accompanying charts.

Overall, the combination of Python crawling and FineBI visualization provides a practical workflow for extracting and analyzing UGC platform data without deep programming expertise.