Original Data and Results：Creator-driven human-computer interaction on UGC video platforms

Description

This dataset is collected from Bilibili, a mainstream UGC video platform in China, using Python crawlers and official open APIs. The final dataset consists of 353,501 video records created by 25,147 unique content creators, with multi-layered observational indicators covering both creator-level and video-level attributes, designed to empirically explore the correlations between creator-initiated interactive widgets and two core user engagement outcomes: online tipping (video coins) and comment-based content co-creation. The data collection process starts with generating billions of random user IDs to verify valid platform accounts. We only retain creators who have published at least one video, and remove invalid samples including removed videos, paywall-locked content and records with missing features. The core independent variables are two binary dummy variables representing affective interaction and cognitive interaction, operationalized by Bilibili’s built-in triple-interaction pop-up and voting pop-up respectively. The dependent variables are the number of coins and comments each video receives, log-transformed to mitigate skewness and heteroskedasticity. Multi-dimensional control variables are included: video-level covariates cover video resolution, publication duration, video length and total views; creator-level covariates include total upload quantity, gender dummy variables, follower count, account level, VIP membership and official verification tier. We also label videos as entertainment or knowledge categories to test content-type moderation, and add a grouping indicator to identify creators who deployed interactive widgets for cross-content spillover analysis.

Institutions

• Beihang University

  Beijing, Beijing

Categories

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