A Review of the Application of Python Crawlers in the Collection of Social Media Business Data

Short videos and graphic social platforms host massive public user-generated content, including consumer preferences, market sentiment and product reviews, serving as vital data sources for market research and business analytics. Python crawlers, with open access, zero cost, strong flexibility and easy deployment, are widely adopted for automated social media business data gathering. This study adopts literature review, case induction and comparative analysis to organize the technical framework of Python crawlers for social media data collection. It compares automated crawling and manual data acquisition in efficiency, cost and accuracy, and summarizes cross-industry commercial applications. The results reveal that crawler applications encounter technical barriers including platform anti-scraping mechanisms, data noise and dynamic webpage parsing difficulties, alongside compliance risks from platform terms and personal data regulations, which hinder data research by small- and medium-sized merchants. Targeting SMEs' constraints of limited technical skills, high research costs and insufficient compliant collection schemes, this paper proposes a low-cost, operable and risk-controlled social media data collection solution. Theoretically, it optimizes the analytical framework for crawler commercial scenarios, adds research on lightweight compliant data gathering, and compensates prior studies' overemphasis on technical implementation while ignoring SMEs' business contexts and compliance requirements, enriching social media data mining research. Practically, the findings offer guidance for crawler beginners, support micro and small enterprises in public opinion tracking, user profiling and competitor analysis, deliver affordable implementation pathways, and facilitate refined business decision-making, possessing promising application value.