How do Students Engage with Fake news and Deepfakes in Korea?
- Alternative Title
- 한국에서 학생들은 가짜 뉴스와 딥페이크에 어떻게 처리하는가? PLS-SEM 분석 및 반구조적 인터뷰 결과
- Abstract
- The menace of disinformation online has garnered a lot of attention in the media, and plausible solutions to prevent misinformation have often fallen short.
Although fake news has been around for centuries, its spread is now more dangerous than ever before due to the speed with which it can be produced and disseminated. Deepfakes, doctored videos, audios, or images that employ artificial intelligence, have emerged at this point in technical progress. That is how disinformation has become a major worry in recent years all around the world. Media users have become accustomed t reading or seeing o false information. As a result of this research, the reader will receive an authorised literature review summarizing the present state of fake news and deepfakes among Korean and foreign students in Korea.
The purpose of this study is to examine the concept and definition of fake news, with a focus on misinformation/false information, as well as how the society could respond to the social reality distortion and democratic harm caused by information distortion such as fake news.
The Korean social context, in which false news was generated and spread, was investigated from the standpoint of datafication, and the concept of fake news was examined based on the degree of facticity and intent to deceive.
I believe that individuals have the potential to play a key role in stemming the spread of misinformation in this post-truth era. A survey (n = 1600) was conducted and Interviews (n = 10) were completed to identify the factors that predict (i) students' social media usage patterns, (ii) their capacity to discern fake news or deepfakes on social media, and (iii) their news sharing intention. According to the findings, information-seeking, parasocial interaction, social tie diversity, and SNS dependency are all linked to both verification and fake news sharing intention
온라인 허위정보의 위협은 지금까지 여러 매체를 통해 크게 다루어져 왔으며, 이에 걸맞은 오보 방지 해결책은 여전히 미흡한 실정이다.
가짜뉴스는 수 세기 동안 계속되어 왔지만 오늘날의 가짜뉴스 확산은 생산 및 전파 속도 면에서 그 어느 때보다 위험성이 크다. 현재 기술의 진보로 인공지능을 활용한 딥페이크 나 조작된 동영상, 음성, 이미지가 생겨나고 있다. 이로 인해 최근 몇 년간 허위정보는 전 세계의 중대 문제로 인식되어 왔다. 매체 이용자들은 허위 정보를 읽고 보는 것에 익숙해져 있다. 본 연구는 한국에 있는 외국인과 한국인 학생의 가짜뉴스 및 딥페이크를 대하는 양상에 대한 향후 문헌조사에 기여할 것이다.
이 연구는 오보/허위 정보를 중심으로 가짜뉴스의 개념과 정의를 고찰하고, 가짜뉴스와 같은 정보 왜곡으로 야기된 사회적 현실 왜곡 및 민주주의 피해에 대한 사회의 대응 방안을 탐색하는 것을 목적으로 한다.
이를 위해 한국 내 허위 뉴스 생성 및 전파의 사회적 맥락을 데이터화 관점으로 조사하였으며, 사실성 및 기만 의도의 정도에 기반하여 가짜뉴스의 개념을 정립하였다.
연구자는 이러한 탈 진실 시대에 개인이 오보 확산 저지를 위한 핵심 역할을 맡을 수 있다는 점을 강조한다. 본 연구는 설문조사(n = 1600)와 면담(n = 10) 수행의 방법으로 (i) 학생들의 소셜 미디어 사용 양태, (ii) 학생들의 소셜 미디어 가짜뉴스 또는 딥페이크 식별 능력, (iii) 뉴스를 공유할 의사 여부를 예측할 수 있는 요인을 파악하였다. 연구 결과에 따르면 정보 탐색, 준사회적 상호작용, 사회적 유대 다양성, SNS 의존도 모두가 가짜뉴스 공유 및 검증 의사와 관련성을 보였다.
- Issued Date
- 2022
- Awarded Date
- 2022. 2
- Type
- Dissertation
- Publisher
- 부경대학교
- Alternative Author(s)
- 벤기다 사브리나
- Affiliation
- 부경대학교 대학원
- Department
- 대학원 신문방송학과
- Advisor
- 김용호
- Table Of Contents
- CHAPTER 1: INTRODUCTION 1
1.1. BACKGROUND OF THE STUDY 1
1.2. PURPOSE OF THE STUDY 10
1.3. PROBLEM STATEMENT AND RESEARCH QUESTIONS 11
1.4. RESEARCH METHODOLOGY 12
1.5. SIGNIFICANCE OF THE STUDY 13
1.6. SUMMARY OF CHAPTERS 14
CHAPTER 2: THE DARK SIDE OF FAKE NEWS AND ITS CONSEQUENCES ON MEDIA USERS 18
2.1. ALL ABOUT FAKE NEWS 19
2.1.1. WHAT IS “FAKE NEWS” 19
2.1.2. SPREAD OF FAKE NEWS 27
2.1.3. FAKE NEWS AND TRUST 29
2.1.4. IS FAKE NEWS SPREADING A PROBLEM 30
2.2. PREVIOUS STUDIES ON FAKE NEWS VERIFICATION/IDENTIFICATION AND SHARING 31
2.3. FACTCHECKING IN KOREA 35
2.3.1. UNDERSTANDING THE JOURNALISTIC PRACTICE 35
2.3.2. MEDIA USERS AS FACT-CHECKERS 41
2.3.3. NEWS VERIFICATION METHODS 44
2.3.4. NO NEWS VERIFICATION BEHAVIOR 46
2.4. THE ROLE OF STUDENTS IN NEWS SHARING 47
2.5. DECEPTIVE DEEPFAKES TECHNOLOGY 51
2.5.1. WHAT IS DEEPFAKE 52
2.5.2. DEEPFAKE IMPACT: DECEPTION, UNCERTAINTY, AND TRUST 55
2.6. CONCLUSION 56
CHAPTER 3: RESPONSIBILITIES OVER CURBING DISINFORMATION 58
3.1. FIGHTING AGAINST INFORMATION POLLUTION 58
3.2. STUDENTS’ DIGITAL INFORMATION LITERACY SKILLS 60
3.2.1. INFORMATION SEEKING SKILLS 60
3.2.2. INFORMATION SHARING SKILLS 61
3.2.3. INFORMATION VERIFICATION SKILLS 61
3.3. REGULATIONS AND POLICIES REGARDING FAKE NEWS IN KOREA 62
3.4. PROMOTION POLICY AND VOLUNTARY RESTRICTIONS 66
3.5. TECH GIANTS AND FAKE NEWS: FACEBOOK AND VODAFONE 69
3.6. CONCLUSION 72
CHAPTER 4: THESIS THEORETICAL FRAMEWORK 73
4.1. THESIS THEORIES 73
4.1.1. INFORMATION AND THE EPISTEMOLOGY OF TESTIMONY 74
4.1.2. USES AND GRATIFICATION THEORY (UGT) 75
4.1.3. THEORY OF REASONED ACTION (TRA) 78
4.1.4. SOCIAL COGNITIVE THEORY 79
4.2. SUMMARY OF THEORIES USED 81
4.2.1. SOCIAL MEDIA INFORMATION TRUST 81
4.2.2. SOCIAL TIE VARIETY 83
4.2.3. PERCEIVED MEDIA CREDIBILITY 84
4.2.4. FAKE NEWS AWARENESS/ FAKE NEWS KNOWLEDGE 87
4.2.5. VERIFICATION BEHAVIOR 89
4.3. SUMMARY OF RESEARCH HYPOTHESES 91
4.3.1. FAKE NEWS HYPOTHESES 91
4.3.2. DEEPFAKE HYPOTHESES 93
4.4. CONCLUSION 95
CHAPTER 5: RESEARCH METHODOLOGIES 96
5.1. WHY A MIXED METHODS APPROACH 96
5.1.1. DEFINITION OF A MIXED APPROACH 96
5.1.2. ADVANTAGES AND DISADVANTAGES 97
5.1.3. QUAN-QUAL VS QUAL-QUAN 98
5.2. QUANTITATIVE ANALYSIS 101
5.2.1. WHY A QUANTITATIVE METHODOLOGY 101
5.2.2. WHY INCLUDE FOREIGN STUDENTS IN THE RESEARCH 101
5.2.3. QUESTIONNAIRE DEVELOPMENT 104
5.2.4. DATA COLLECTION 107
5.2.4.1. Population 109
5.2.4.2. Restrictions 110
5.2.4.3. Costs 110
5.2.4.4. Tools used to spread the questionnaire 111
5.2.4.5. Why PLS-SEM as a measurement model 112
5.2.5. PRELIMINARY DATA SCREENING AND CLEANING 114
5.3. QUALITATIVE ANALYSIS 116
5.3.1. QUALITATIVE METHODS OF DATA COLLECTION 116
5.3.2. ARCHIVAL RESEARCH 118
5.3.3. SEMI-STRUCTURED INTERVIEWS 118
5.3.4. LIST OF THE INTERVIEWEES 121
5.4. CONCLUSION 124
CHAPTER 6: DATA ANALYSIS AND FINDINGS 125
6.1. SMARTPLS-SEM SOFTWARE PROCEDURE 125
6.2. MEASUREMENTS 127
6.3. ASSESSMENT OF MEASUREMENT MODEL 128
6.3.1. CONVERGENT VALIDITY TEST 128
6.3.2. DISCRIMINANT VALIDITY 130
6.3.2.1 Fornell and Larcker Matrix 131
6.3.2.2. Cross loading results 132
6.3.2.3. Heterotrait-Monotrait Ratio (HTMT) 133
6.4. STRUCTURAL MODEL 134
6.4.1. COEFFICIENT OF DETERMINATION R² 134
6.4.2. SRMR 135
6.4.3. PREDICTIVE RELEVANCE Q² 135
6.4.4. NORMED FIT INDEX (NFI) OR BENTLER AND BONETT INDEX 136
6.4.5. COMMON METHOD BIAS 137
6.4.6. GOODNESS OF FIT 139
6.4.7. HYPOTHESIS TESTING 139
6.4.7.1. Research model with path coefficients 139
6.4.7.1.1. Research model with results (Korean students) 140
6.4.7.1.2. Korean hypothesis relationships 142
6.4.7.1.4. Research model with results (foreign students) 143
6.4.7.1.5. Hypothesis testing (foreigners) 144
6.5. INTERPRETATION OF HYPOTHESES 145
6.6. DISCUSSION 162
CHAPTER 7: QUALITATIVE ANALYSIS FINDINGS 171
7.1. PARTICIPANT’S DEMOGRAPHICS CHARACTERISTICS 171
7.2. USE OF ONLINE NEWS SOURCES 173
7.3. TRUSTED NEWS SOURCES 177
7.4. FREQUENCY OF LOGGING, AND OF SHARING POSTS 179
7.5. WHY DO STUDENTS USE OSM 182
7.6. CREDIBILITY AND VERIFICATION OF ONLINE INFORMATION 184
7.7. DEEPFAKES 185
7.7.1. WHAT ARE DEEPFAKES' GREATEST FEARS 185
7.7.2. DEEPFAKE EXPERIMENT 189
7.7.2.1. Intersection over Union 189
7.7.2.2. Deepfake sample test: Image 1, 2 and 3 189
7.8. MAIN RESULTS 192
7.9. DISCUSSION 204
CHAPTER 8 DISCUSSION AND MAIN CONCLUSION 207
8.1. INTERPRETATIONS 207
8.2. IMPLICATIONS 212
8.3. LIMITATION OF THE STUDY 213
8.4. THESIS CONTRIBUTION AND FURTHER RESEARCH REFERENCES 214
BIBLIOGRAPHY 216
- Degree
- Doctor
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