Research Article

The Research on the Social Media Analytica Model Apply on Barbie Culture Influences in Society

Haoxi XieUniversity of Putra Malaysia*

* Corresponding author: [email protected]

Abstract

Barbie has long been one of the world’s most influential icons for women; it was launched in 1959 by Ruth Handler. As a successful fashion doll model, she is a figurehead of the brand, leading females to have independence in life. Her influence has changed the social status and life of female groups. This paper starts with the data analysis of the first Barbie live-action movie in 2023. Then, it analyzes and predicts the number of plays on the Barbie YouTube channel. Use Python data analysis implementation to analyze text movie reviews. This Python data technique includes text analysis, visualization, sentiment analysis, topic modeling, keyword extraction, and machine learning prediction. Further, the author combine the analysis results and study the social background of the female group in different regions and discuss the influence of Barbie culture and women’s social status in detail. Our side will further analyze the final result and obtained it’s on society, obtain its impact on society.

Keywords: Barbie; female; data analysis; python
Published: December 7, 2023
DOI: 10.54254/2753-7064/20/20231377
Volume: CHR Vol.20
pp. 228-237
Download PDF

References

  1. Robin,D. (2016)The Evolution of an Icon: A Comparison of the Values and Stereotypes Reflected in the Original 1959 Barbie Doll and the Curvy 2016 Barbie Doll.
  2. Ahmad, H. (2020)Influencer of Barbie Doll Movies on Women in Pakistan.Global Multimedia Review, 3.1, 52-64.
  3. Reilly,N.(2019).Women, Gender, and International Human Rights: Overview. International Human Rights of Women. Springer Nature Singapore Pte Ltd. 1-18. https://doi.org/10.1007/978-981-10-8905-3.
  4. Aakansha,G.,Katarya,R.(2020) Social media based surveillance systems for healthcare using machine learning: A systematic review. Journal of Biomedical Informatics, Volume 108, https://doi.org/10.1016/j.jbi.2020.103500.
  5. Boeun,K.,Ryu,K.H.,Heo,S.M.(2022)Mean squared error criterion for model-based design of experiments with subset selection.Computers & Chemical Engineering, Volume 159, https://doi.org/10.1016/j.compchemeng.2022.107667.
  6. Bzdok,D.,Altman,N.,Krzywinski,M. (2018)Statistics versus machine learning. Nat Methods, 15(4),233-234. doi: 10.1038/nmeth.4642. Epub 2018 Apr 3. PMID: 30100822; PMCID: PMC6082636.
  7. Zachlod, Cécile & Samuel, Olga & Ochsner, Andrea & Werthmüller, Sarah. (2022)Analytics of social media data – State of characteristics and application. Journal of Business Research, 144, 1064-1076. 10.1016/j.jbusres.2022.02.016.
  8. Worobey,J.,Worobey,HS. (2014)Body-size stigmatization by preschool girls: in a doll’s world, it is good to be “Barbie”. Body Image, 11(2),171-4. doi: 10.1016/j.bodyim.2013.12.001. Epub 2014 Jan 4. PMID: 24394637.
  9. Vairetti,C.,Eugenio Martínez-Cámara, Sebastián Maldonado, Victoria Luzón, Francisco Herrera.(2020) Enhancing the classification of social media opinions by optimizing the structural information. Future Generation Computer Systems, Volume 102,838-846. https://doi.org/10.1016/j.future.2019.09.023.
  10. Gehlenborg,N., Wong, B.(2012) Heat maps. Nat Methods 9, 213. https://doi.org/10.1038/nmeth.1902
  11. Jordan,MI.,Mitchell,TM.(2015) Machine learning: Trends, perspectives, and prospects. Science,349(6245),255-60. doi: 10.1126/science.aaa8415. PMID: 26185243.
  12. M.A.Al-Garadi et al.(2019) Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges. In IEEE Access, vol. 7, 70701-70718. doi: 10.1109/ACCESS.2019.2918354.
  13. Wnkhade, Mayur & Rao, Annavarapu & Kulkarni, Chaitanya. (2022) A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55. 1-50. 10.1007/s10462-022-10144-1.
  14. Xu, Q.W.,& Chang, Victor & Jayne, Chrisina. (2022) A systematic review of social media-based sentiment analysis: Emerging trends and challenges. Decision Analytics Journal, 3. 100073. 10.1016/j.dajour.2022.100073.
  15. Michaelidou, Nina & Micevski, Milena. (2018) Consumers’ ethical perceptions of social media analytics practices: Risks, benefits and potential outcomes. Journal of Business Research, 104. 10.1016/j.jbusres.2018.12.008.