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http://dx.doi.org/10.5392/IJoC.2021.17.4.052

Rethinking the US Presidential Election: Feminism and Big Data  

CHUNG, Sae Won (Division of International and Area Studies, Pukyong National University)
PARK, Han Woo (Department of Media and Communication, Yeungnam University)
Publication Information
International Journal of Contents / v.17, no.4, 2021 , pp. 52-61 More about this Journal
Abstract
The 2020 US Presidential Election was a highly-anticipated moment for our global society. During the election period, the most intriguing issue was who would be the winner-Trump or Biden? Among the possible main themes of the 2020 election, from the COVID-19 pandemic to racism, this study focused on feminism ('women') as a main component of Biden's victory. To explore the character of Biden's supporters, this paper focused on internet spaces as a source of public opinion. To guide the data analysis, this study employed four indices from empirical studies on Big Data analytics: issue salience, attention diversity, emotional mentioning, and semantic cohesion. The main finding of this study was that the representative keyword 'women' appeared more prevalently within content related to Biden than Trump, and the keyword pairs indicated that female voters were the main reason for Trump's failure but the root cause of Biden's victory. The results of this study indicated the role of the internet as a forum for public opinion and a fountain of political knowledge, which requires more rigorous investigation by researchers.
Keywords
US Presidential Election; Issue Salience; Attention Diversity; Emotional Mentioning; Semantic Cohesion;
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1 A. Brodeur, L. Baccini, and S. Weymouth, "How COVID-19 led to Donald Trump's Defeat," The Conversation. https://theconversation.com/how-covid-19-led-to-donald-trumps-defeat-150110, accessed July 20, 2021.
2 Z. Cohen, J. Hansler, K. Atwood, V. Salama, and S. Murray, "Trump Administration begins formal withdrawal from World Health Organization," CNN.com https://edition.cnn.com/2020/07/07/politics/us-withdrawing-worldhealth-organization/index.html accessed July 20, 2021.
3 H. W. Park, and Y. Lim, "Do North Korean Social Media show signs of Change?: An Examination of a YouTube Channel Using Qualitative Tagging and Social Network Analysis," Journal of Contemporary Eastern Asia, vol. 19, no. 1, pp.123-143, 2020, doi: https://doi.org/10.17477/JCEA.2020.19.1.123   DOI
4 R. Igielnik, "Men and Women in the US Continue to Differ in Voter Turnout Rate, Party Identification," Pew Research Institute, https://www.pewresearch.org/fact-tank/2020/08/18/men-and-women-in-the-u-s-continue-todiffer-in-voter-turnout-rate-party-identification/, accessed July 20, 2021.
5 H. Allcott, L. Boxell, J. Conway, M. Gentzkow, M. Thaler, and D. Yang, "Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic," J. Pub. Eco., vol. 191, no. 104254., 2020, doi: https://doi.org/10.1016/j.jpubeco.2020.104254   DOI
6 C. Chung, J. Biddix and H. Park, "Using digital technology to address confirmability and scalability in thematic analysis of participant-provided data," The Qualitative Report, vol. 25, no. 9, pp. 3298-3311, 2020, doi: https://doi.org/10.46743/2160-3715/2020.4046   DOI
7 S. Yoon, and S. Chung, "The EU's Public Diplomacy in Asia and the World through Social Media: Sentiment and Semantic Network Analyses of Official Facebook Pages of European External Action Service and EU Delegation to the Republic of Korea," Journal of Contemporary Eastern Asia, vol. 19, no. 2, pp.234-263, 2020, doi: https://doi.org/10.17477/JCEA.2020.19.2.234   DOI
8 G. Milam, "US election 2020: Why suburban white women are so important to deciding the result," Skynews, https://news.sky.com/story/us-election-2020-why-suburban-white-women-are-so-important-to-deciding-the-result-12118643 accessed July 20, 2021.
9 L. Doggett, "Timeline of Trump's Coronavirus Responses," Lloyd Doggett Official Blog, https://doggett.house.gov/media-center/blog-posts/timeline-trump-s-coronavirus-responses, accessed July 20, 2021.
10 S. Chira, "Donald Trump's Gift to Feminism: The Resistance," Daedalus, vol. 149, no. 1. 2020, doi: https://doi.org/10.1162/daed_a_01774   DOI
11 J. Kwak, and S. Cho, "Analyzing Public Opinion With Social Media Data During Election Periods: A Selective Literature Review," Asian Journal for Public Opinion Research, vol. 5, no. 4, pp. 285-301, 2018, doi: https://doi.org/10.15206/ajpor.2018.5.4.285   DOI
12 H. W. Park, S. Park, and M. Chong, "Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea," Journal of Medical Internet Research, vol. 22, no. 5, 2020, doi: https://doi.org/10.2196/18897   DOI
13 V. Chykina and C. Crabtree, "Using Google Trends to Measure Issue Salience for Hard-to-Survey Populations, Socius, vol.4., pp. 1-3., 2018, doi: https://doi.org/10.1177/2378023118760414   DOI
14 E. Delmore, "This is how women voters decided the 2020 election." NBC News, https://www.nbcnews.com/knowyour-value/feature/how-women-voters-decided-2020-election-ncna1247746, accessed July 20, 2021.
15 L. Vavreck, "It's Not Just Suburban Women. A Lot of Groups have turned against Trump," New York Times, https://www.nytimes.com/2020/11/02/upshot/election-polling-trump-women.html accessed July 20, 2021.
16 S. Park, D. Jeong, and H. Park, "Analytical Framework for Evaluating Digital Diplomacy Using Network Analysis and Topic Modeling: Comparing South Korea and Japan," Information Processing and Management, vol. 56., pp. 1468-1483., 2019. doi: https://doi.org/10.1016/j.ipm.2018.10.021   DOI
17 G. Michael, and C. Agur, "The Bully Pulpit, Social Media, and Public Opinion: A Big Data Approach," Journal of Information Technology & Politics, vol. 15, no. 3, pp. 262-277, 2018, doi: https://doi.org/10.1080/19331681.2018.1485604   DOI
18 M. Skoric, J. Liu, and K. Jaidka, "Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis," Information, vol. 11, no. 4, art. no. 187, March 2020, doi: https://doi.org/10.3390/info11040187   DOI
19 M. Thelwall, Big data and social web research methods, University of Wolverhampton, Wolverhampton, 2018. [Online] Available: http://www.scit.wlv.ac.uk/~cm1993/papers/IntroductionToWebometricsAndSocialWebAnalysis.pdf
20 S. Jalali, M. Jafar, H. Park, I. Vanani, and K.-H. Pho, "Research trends on big data domain using text mining algorithms," Digital Scholarship in the Humanities, art. no. fqaa012, April 2020, doi: https://doi.org/10.1093/llc/fqaa012 [Online] Available: https://academic.oup.com/dsh/advance-articleabstract/doi/10.1093/llc/fqaa012/5817944?redirectedFrom=fulltext   DOI
21 S. Park and H. Park, "Diffusion of cryptocurrencies: Web traffic and social network attributes as indicators of cryptocurrency performance," Quality & Quantity, vol. 54, no. 1, pp. 297-314, 2020, doi: https://doi.org/10.1007/s11135-019-00840-6   DOI
22 B. Norambuena, E. Lettura and C. Villegas, "Sentiment Analysis and Opinion Mining Applied to Scientific Paper Reviews," Intelligent Data Analysis, vol. 23, no. 1, pp. 191-214 doi: https://doi.org/10.3233/IDA-173807   DOI
23 World Health Organization. "WHO Coronavirus Disease (COVID-19) Dashboard," https://covid19.who.int/, accessed July 20, 2021.