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Body Shame Twitter Movement Text Mining and Data Analysis by Applying MDCOR
- 1 Shanghai World Foreign Language Academy
- 2 The University of Hong Kong
- 3 Shanghai Qibaodwight High School
* Author to whom correspondence should be addressed.
Abstract
As one of the most heated-discussed movements on Twitter these years, tweets in #bodyshame has been complicated and debatable. To explore the alternation of attitudes toward #bodyshame and how it has changed in the past decade, the study analyzes the data collected from three-time frames: 2012, 2017, and 2022, respectively. The increasing popularity and awareness of #bodyshame of the public are shown in this work. By applying MDCOR, an open-ended responses classification framework to data analysis, this paper was able to conclude the gradually alternating trend of Twitter users' attitudes toward body shame. Social media data analysis helps the understanding of the general development trend of #bodyshame movement, which ultimately provides a comprehensive overview of people’s acceptance and opinions towards various body types. This study’s data-oriented research on attitudes towards body shame is profound in meaning.
Keywords
body shame, Twitter, MDCOR, text mining, data analysis
[1]. Ariane Resnick, C. N. C. (2022), What is body shaming? very well mind. http://www.verywellmind.com/what-is-body-shaming-5202216
[2]. Erin Nolen(2019), Is the body positivity Social Movement Toxic?, UT News. https://news.utexas.edu/2019/12/18/is-the-body-positivity-social-movement-toxic/
[3]. A.M. Chiesi(2001), Network Analysis, ScienceDirect. https://www.sciencedirect.com/topics/social-sciences/network-analysis
[4]. Freeman, Linton C (2004), The development of social network analysis, Empirical Press.
[5]. Wikipedia org(2022), Social Network Analysis, Wikipedia. https://en.wikipedia.org/wiki/Social_network_analysis#History
[6]. Manuel S. González Canché(2020), Machine Driven Classifification of Open-ended Responses (MDCOR).
Cite this article
Li,X.;Li,R.;Zheng,Y. (2023). Body Shame Twitter Movement Text Mining and Data Analysis by Applying MDCOR. Lecture Notes in Education Psychology and Public Media,10,7-13.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the International Conference on Social Psychology and Humanity Studies
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