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Chen, H., Hsu, J., Hsieh, J., Hsu, H., Chang, C. and Lin, Y. (2021) Predictive Maintenance of Abnormal Wind Turbine Events by Using Machine Learning Based on Condition Monitoring for Anomaly Detection. Journal of Mechanical Science and Technology, 35, 5323-5333.
https://doi.org/10.1007/s12206-021-1105-z

has been cited by the following article:

• TITLE: Research on Influencing Factors of Short-Form Video Marketing Effect in Mobile Home Sell

  AUTHORS: Bakirova Tatyana

  KEYWORDS: Short-Form Video, SFV Approach, Marketing

  JOURNAL NAME: Open Access Library Journal, Vol.12 No.4, April 24, 2025

  ABSTRACT: Short-form video (SFV) marketing serves as an essential consumer engagement tool that proves vital within the mobile home sales industry. This research uses quantitative methods to study how SFV engagement metrics affect viewer interest between Douyin, TikTok, YouTube Shorts and Facebook Reels. Web scraping tools including APIFY for Douyin and Facebook and Python scripts for TikTok and YouTube extracted 185 videos across all platforms. The research analysis combined descriptive statistics with sentiment analysis through NLP (VADER) and regression modelling to determine the influence between engagement rates and consumer interest. The ANOVA test evaluated how strategy for success variations appeared across different platform frameworks. The research data shows that platform algorithms together with content quality create strong relationships between likes and views but weak relationships between comments. The study findings generate practical suggestions that help marketers enhance their SFV approach.