Analyzing motivations and negative implications for cancel culture engagements through natural language processing: A cross-country comparative study
- Alternative Title
- 거부 문화의 참여 동기 및 부정적 함의에 관한 자연어 처리분석 연구: 8개국 트위터를 중심으로 한 비교분석
- Abstract
- This study uses natural language processing methods to investigate text data on cancel culture to elucidate language uses that indicate the motivation for such activism in different countries and some of the negative implications of such communication on social media. Cancel culture is a kind of activism that utilizes social media to organize and reach people. In recent years, the hashtags ‘#Cancel_’ and ‘#boycott’, used to advance cancel culture, have become very popular. The activism has elicited a lot of media discourses, academic studies, and, especially, theoretical postulations. Many of the previous research efforts have explored the meaning of cancel culture and the motivations behind it, concluding that it proceeds from the concern for social justice, driven by the woke movement that started in the USA around 2017/18 and gained worldwide spread over time. As cancel culture continues to gain global prominence, the question needs to be asked if the activism is still inspired by the goal of achieving a fairer society. This study is set to examine the similarity of cancel culture as a form of activism aimed at achieving a fairer society across different countries, as well as the motivating factors for the activism and the implication (negative) of these kinds of communication on people who engage in the conversational exchanges about it. I propose that beyond wokeness/social justice, diverse motives drive cancel culture in the varying contexts where it is popular, some of which may be laudable, and some outright dreadful. I shall mine cancel culture data on Twitter and explore the conversations with a view to underscoring linguistically marked motivating factors that participants actively or passively attribute to their involvement in these engagements and some of the negative implications of the types of communication they are involved in the context of cancel culture engagements. The semantic markers will help to explicate the motivating factors and the implications which are bellied within the texts of the cancel culture conversations. I shall utilize various functions in natural language processing (NLP) to examine the text data to explicate the motivations for cancel culture and underscore the implication of such for society. The data for the study are user-generated comments on Twitter with cancel culture hashtags indicating their context, made between 2018 and 2022. I select the countries of South Korea, India, the Philippines, the USA, the UK, Nigeria, South Africa, and Brazil for the study. These countries represent different contexts where cancel culture is popular on Twitter and in mainstream media discourse. Text mining (TM) and natural language processing (NLP) methods are used for the analysis of this study, to examine the propositions related to the study's objective. The analyses will be implemented with R and VosViewer software. I shall conduct text similarity analysis, word network analysis, dictionary analysis, word frequencies, keyword-in-context, other text summaries, and visualizations. The analyses will help shed light on the various underlying but often unrecognized motivating factors behind cancel culture activism and the implications of negative types of communication made in the context of cancel culture engagements.
- Issued Date
- 2023
- Awarded Date
- 2023-08
- Type
- Dissertation
- Keyword
- Cancel culture, wokeness, woke, boycott, shaming culture, free speech, hate speech, motivations,
- Publisher
- 부경대학교
- Affiliation
- Pukyong National University, Graduate School
- Department
- 미디어커뮤니케이션학과
- Advisor
- 한혜경
- Table Of Contents
- Chapter I. Introduction and Background to the Study 1
1. Cancel culture activism in the digital social worlds 1
2. Purpose of study 7
3. Research questions 8
Chapter II. The review of related literature 10
1. Cancel culture: Understanding the phenomenon 10
2. Evaluating previous studies on cancel culture 12
3. Theoretical perspectives on motivation for cancel culture 16
4. Cancel culture and context 18
4.1. Cultural dynamics 19
4.2. Political and ideological divides 20
4.3. Social dynamics and power structures 22
5. Categories of motivations/motivating factors for cancel culture 23
5.1. Wokeness 23
5.2. Politics 24
5.3. Nationalism/patriotic sentiment 24
5.4. Normative/traditional activism 24
5.5. Cultural/Moral/Ethical values 25
5.6. Free speech/freedom of expression 26
6. Categories implications for the negative of cancel culture conversations 26
6.1. Hatred/toxicity 27
6.2. Stereotyping 27
6.3. Polarization 28
6.4. Prejudice/discrimination 28
6.5. Bullying/verbal abuse 29
6.6. Mockery/shaming/trolling/name-calling 29
6.7. Defamation/doxing/blackmail 30
Chapter III. Methodology 32
1. Opinion mining and natural language processing 32
2. Data selection and collection 32
3. Data presentation 35
4. Data preprocessing 36
5. Data analysis methods 37
5.1. Document similarity: Text similarity analysis 37
5.2 Motivating factors for participation in cancel culture conversations 39
5.2.1. LDA analysis 39
5.2.1. Dictionary analysis 40
5.2.3. Word network/wordcloud analysis 41
5.2.4. Word frequencies 42
5.2.5. Keywords-in-context 44
Chapter iv. Findings and analysis of the data 46
1. Investigating the mutuality of cancel culture conversations 46
1.1. High similarity 47
1.2. Average similarity 50
1.3. Low similarity 53
1.4. Summary of the findings on the mutuality of the cancel culture culture conversations 55
2. Explicating motivating factors for cancel culture 56
2.1. Presentation of the result of the LDA analysis 57
2.2. Dictionary analysis: Explicating different kinds of motivating factors for cancel culture 59
2.2.1 The motivation for cancel culture in South Korea 60
2.2.2. The motivation for cancel culture in India 62
2.2.3. The motivation for cancel culture in the Philippines 64
2.2.4. The motivation for cancel culture in the United States of America 66
2.2.5. The motivation for cancel culture in the United Kingdom 69
2.2.6. The motivation for cancel culture in Nigeria 71
2.2.7. The motivation for cancel culture in South Africa 72
2.2.8. The motivation for cancel culture in Brazil 76
2.3. Explaining the motivating factors for cancel culture 78
2.3.1. Wokeness as a motivating factor for cancel culture engagement 78
2.3.2. Nationalism as a motivating factor for cancel culture engagement 84
2.3.3. Politics as a motivating factor for cancel culture engagement 87
2.3.4. Moral/ethical/cultural values as a motivating factor for cancel culture engagement 89
2.3.5. Traditional/normative activism as a motivating factor for cancel culture engagement 92
2.3.6. Free speech as a motivating factor for cancel culture engagement 94
2.4. Summary of the result of motivating factors for cancel culture 97
3. Explicating the implications for negative cancel culture conversations 99
3.1. Result of the dictionary analysis, keyword in context and wordfish analysis 99
3.1.1. Hatred/toxicity 99
3.1.2. Stereotyping 101
3.1.3. Polarization 104
3.1.4. Prejudice/discrimination 105
3.1.5. Bullying/verbal abuse 107
3.1.6. Mockery/shaming/trolling/name-calling 109
3.1.7. Defamation/blackmail 111
Chapter v. Summary and Conclusions 114
1. Summary 114
1.1. On the similarity of cancel culture conversations 114
1.2. On the motivating factors for cancel culture 115
1.3. On the implications of negative types of cancel culture conversations 118
2. Conclusions 119
References 122
국문초록 148
- Degree
- Doctor
- Appears in Collections:
- 대학원 > 미디어커뮤니케이션학과
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