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研究生：	度米蒂 Abdul, Meiselina Irmayanti
論文名稱：	網路新聞中的道德恐慌：大流行期間基於網路的詞頻和情感分析的文本挖掘 Moral Panic in Online News: Text Mining for Web-Based Word Frequency and Sentiment Analysis during Pandemic
指導教授：	周麗芳 Chou, Li-Fang
學位類別：	博士 Doctor
系所名稱：	社會科學院 - 心智科學原理與應用國際博士學位學程 International Doctoral Program in Principles and Implications of Mind Sciences
論文出版年：	2025
畢業學年度：	113
語文別：	英文
論文頁數：	177
中文關鍵詞：	道德恐慌、情調、COVID-19 大流行、詞頻、文本挖掘
外文關鍵詞：	Moral panic, sentiment, the COVID-19 pandemic, word frequency, text mining
ORCID：	0000-0003-3993-8509
ResearchGate：	https://www.researchgate.net/profile/Meiselina-Irmayanti
相關次數：	點閱：32 下載：3
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在印尼 COVID-19 大流行期間，線上新聞平台對培育道德恐慌的影響是多方面的。本研究檢視了 2021 年 Kompas.com 的頭條新聞和新聞文章，這些文章是使用 RStudio 進行網頁抓取的。然後，它調查了這些敘述如何加劇社會焦慮和恐懼，從而加劇整個大流行期間的道德恐慌。該研究使用 Voyant Tools 和 VBA 進行了分析，確定了經常使用的術語，例如“COVID”、“病例”和“健康”，這些術語有可能引發恐慌。它還揭示了與負面語氣相關的主導情緒（例如，透過 LIWC 得分 1 到 33）以及焦慮和恐懼等情緒，並透過降神會進行分析。研究結果揭示了疫情各階段媒體引發的道德恐慌的模式。關鍵術語（例如「新冠肺炎」）出現頻率的增加與恐慌加劇密切相關，特別是在 2021 年 6 月和 7 月期間，病毒式媒體內容加劇了公眾的恐懼和焦慮。此外，研究還發現負面情緒激增與公共衛生危機之間存在密切關係，凸顯了媒體在加劇社會焦慮方面的作用。這項開創性的研究透過文本探勘、詞頻和情感分析提供了分析網路媒體敘事的實證框架，為道德恐慌的文獻做出了貢獻。它也為全球突發衛生事件期間的未來媒體實踐提供了寶貴的見解。

The influence of online news platforms in cultivating moral panic during the COVID-19 pandemic in Indonesia is multifaceted. This study examines headlines and news articles from Kompas.com in 2021, which were web-scraped using RStudio. Then, it investigates how these narratives heightened societal anxiety and fear, thereby intensifying moral panic throughout the pandemic. The research identifies frequently used terms, such as "COVID," "cases," and "health," which have the potential to contribute to panic, analyzed using Voyant Tools and VBA. It also uncovers dominant sentiments associated with negative tones (e.g., scoring 1 to 33 via LIWC) and emotions like anxiety and fear, analyzed with Seance. The findings reveal patterns of media-induced moral panic across various phases of the pandemic. Elevated frequencies of key terms (e.g., "COVID") were strongly correlated with heightened panic, particularly during June and July 2021, when viral media content amplified public fear and anxiety. Moreover, the study identifies a strong relationship between surges in negative sentiment and public health crises, highlighting the media's role in exacerbating societal anxieties. This pioneering research contributes to the literature on moral panics by offering an empirical framework to analyze online media narratives through text mining, word frequency, and sentiment analysis. It also provides valuable insights for future media practices during global health emergencies.

Softcover 
Certificate of approval for doctoral dissertation i
ABSTRACT ii
Acknowledgement iv
Table of contents v
List of tables	xi
List of figures xii
List of abbreviations xiii
CHAPTER 1 1
INTRODUCTION 1
1.1	Background and Context	1
Figure 1.1 4
1.2	Problem Statement 4
1.3	Research Questions 5
1.4	Objectives of the Study 5
1)	Identify key terms and phrases 5
2)	Analyze sentiment trends	6
3)	Examine the Relationship Between Word Frequency and Sentiment 6
4)	Map Out the Evolution of Moral Panic	6
1.5	Purposes of the Study 6
1.6	Significance of the Study 7
1.7	Thesis Statement of the Study	7
CHAPTER 2 8
LITERATURE REVIEW	8
2.1	Historical Context and Evolution of Moral Panics Theory 8
2.2	Theoretical Framework 8
2.3	The Role of Media in Shaping Moral Panics	9
2.3.1	Kompas.com as Selected Online News Platform 10
2.4	Moral Panics in the Context of Health Crises 11
Table 2.1 12
2.5	Text Mining and Sentiment Analysis in Studying Moral Panics 16
2.5.1	Text Mining in Moral Panic during the COVID-19 Pandemic 16
Figure 2.1 18
Figure 2.2 19
Figure 2.3 20
2.6	Application of Text Mining and Sentiment Analysis to the COVID-19 pandemic Media Coverage 20
Table 2.2 21
2.7	Current Stage of Knowledge 23
2.8	Research Gap 23
2.9	Key Concepts and Definitions	24
2.10	Conclusion 25
CHAPTER 3 27
METODOLOGY 27
3.1	Research Design	27
3.2	Data Collection 28
3.2.1	Data Source	28
3.2.2	Sampling Strategy 28
3.2.3	Data Gathering 29
3.2.4	Data Source	29
3.3	Analysis Framework 30
3.3.1	The Centralize Term and The Themes 30
3.3.2	Word Frequency	30
3.3.3	Sentiment Analysis 31
Figure 3.1 32
3.4	Validation and Reliability 33
1)	Data cleaning 33
2)	Consistency methodology 34
3)	Cross-tool validation 34
4)	Statistical validation 34
5)	Document the process and settings 34
3.5	Visualization and Interpretation 35
3.6	Research Procedure 35
3.7	Ethical Considerations 36
Figure 3.2 37
CHAPTER 4 38
RESEARCH RESULTS 38
4.1	Data Presentation 38
Table 4.1 38
4.2	Connection of Key Findings 39
4.2.1	Centralize 39
Figure 4.1 40
Table 4.2 41
4.2.2	Trends Visualization: A Year Trend and Comparison Trend 44
1)	A year trend	44
2)	Comparison trend between July 2021 (COVID) and February 2024 (“Jokowi”) 45
Figure 4.2 47
Figure 4.3 48
4.2.3	Word Frequency	48
1)	Word cloud (Cirrus) 48
Figure 4.4 49
Figure 4.5 50
2)	Collocates (Bigram) 51
Figure 4.6 54
Figure 4.7 55
Figure 4.8 58
1)	The result of OHA dataset analyzing in Seance 1.2.0 60
2)	The result of ONA dataset analysis in LIWC-22 62
3)	The result of ONA dataset analysis in Séance 63
Figure 4.9 65
4.3	“Who”: The Top Mentions and The Frequencies in OHA 66
Figure 4.10 66
4.3.1	Pattern and Narrative Identified: PPKM and Restrictions	66
4.3.2	Pattern and Narrative Identified: COVID-19 Vaccination and Health 67
4.3.3	Pattern and Narrative Identified: Educational Impact 67
4.4	“Who”: The Top Mentions and The Frequencies in ONA 67
Figure 4.11 68
4.4.1	Pattern and Narrative Identified: Public Health Management and Government Measures 68
4.4.2	Pattern and Narrative Identified: Societal Impact and Community Responses 69
4.4.3	Pattern and Narrative Identified: Health Infrastructure and Patient Care 69
4.4.4	Pattern and Narrative Identified: Political Messaging and Leadership 69
CHAPTER 5 71
DISCUSSIONS 71
5.1	Overview of Headlines and News Articles Coverage in Kompas.com 71
5.1.1	Public Health Measures 72
5.1.2	Medical and Health 72
5.1.3	Emotional and Social Impact 73
5.1.4	Policy and Governance 73
5.1.5	Economic Impact 74
5.1.6	Vaccination 74
5.1.7	Global and Regional Dimensions 75
5.1.8	Temporal Patterns 76
Figure 5.1 76
5.2	Temporal Dynamics: Yearly and Monthly Comparative Trends 76
5.3	The Top Frequency in Headline 78
5.3.1	Dominance of COVID and Cases 78
5.3.2	Emphasis on Health, Vaccine, and Vaccination 78
5.3.3	Institutional and Policy Framing 79
5.3.4	Societal Disruptions 79
5.3.5	Amplification 79
5.4	The Keyword Collocates and Their Implications	80
5.4.1	Sentiment and Linguistic Framing	81
5.4.2	Social and Cultural Implications 81
Figure 5.2 82
5.5	The Keyword Collocates and Their Implications	82
5.5.1	Sentiment and Linguistic Framing	83
5.5.2	Social and Cultural Implications 83
5.6	Top Mentions and Frequencies of “Who” in Driving Moral Panic 84
5.6.1	Mentions in Headlines: Personalization and Authority Figures 84
5.6.2	Mentions in News Articles: Collectivization and Generalized Impact 85
5.7	Sentiment Patterns in News Headlines	86
5.7.1	LIWC Results: Negative and Positive Tone with Anxiety Sentiment 86
5.7.2	Seance Results: Negative and Positive Tone with Fear Sentiment	87
5.7.3	Comparison of Anxiety and Fear Sentiments 87
5.7.4	Sentiment Patterns and Indicators of Moral Panic 88
5.8	Sentiment Patterns in News Articles 88
5.8.1	LIWC Results: Tone Proportion and Anxiety Sentiment 88
5.8.2	Seance Results: Tone Proportion and Fear Sentiment 89
5.8.3	Comparative Analysis: Anxiety and Fear in Media Narratives	90
Figure 5.3 91
5.9	Implications of Results 91
5.9.1	For Media Studies 91
5.9.2	For Public Health (Practitioners) 92
5.9.3	For Psychological Research (Researchers) 93
5.9.4	For Decision-Making	93
5.9.5	The Role of Indonesia’s Ethical and Policy Framework 94
5.9.6	Implications for Media Coverage during the COVID-19 Pandemic 96
5.10	Strengths of Study and Limitations 97
5.10.1	Strengths of Study 97
5.10.2	Limitations 98
CHAPTER 6 100
CONCLUSION 100
6.1	Summary of Key Points 100
6.2	Contribution to the Field 102
Figure 6.1 103
6.3	Recommendations 105
6.4	Future Research Directions 106
6.5	Conclusion 107
REFERENCES 108
Appendix 1. Code for OHA data scraping using RStudio	131
Appendix 2. Code for ONA data scraping using RStudio	132
Appendix 3. Code for getting sheets using VBA 133
Appendix 4. Code for merging worksheet using VBA 134
Appendix 5. Code for daily word frequency of OHA using VBA 135
Appendix 6. Code for deleting worksheet using VBA 136
Appendix 7. Code for merging files in folder using VBA 137
Appendix 8. Scatter plot of VBA Tool and Voyant Tools results 138
Appendix 9. Summary table of the differences between VBA-frequency and VY frequency 139
Appendix 10. Summary table of sentiment results of ONA datasets segment 1 utilizing LIWC 142
Appendix 11. Summary table of sentiment results for ONA datasets utilizing Seance 144
Appendix 12. Sample of OHA page in Kompas.com, 1 July 2021 146
Appendix 13. Sample of ONA page in Kompas.com, 5 January 2021 147
Appendix 14. OHA’s top frequency word “COVID-19” in Kompas.com 2021 analyzing in VBA	148
Appendix 15. OHA Top Frequency Word “Jokowi” in Kompas.com February 2021 Analyzing in VBA 149
Appendix 16. “Who”: Top Mentions and Frequencies in OHA 150
Appendix 17. “Who”: Top Mentions and Frequencies in ONA 155
Appendix 18. Examples of Positive Tone Words in the Media	162
                                    

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