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題名 檢視印尼ChatGPT採用、風險和信任議題之網路討論、 具影響力者及情緒：大數據分析觀點
Examining online discussions, influential actors, and sentiments of ChatGPT adoption, risk and trust in Indonesia: A big data analytics perspective作者 李勇俊
Putra, Rio Oktora Nanda貢獻者 林翠絹
Lin, Trisha Tsui-Chuan
李勇俊
Putra, Rio Oktora Nanda關鍵詞 ChatGPT
X.com
印尼
ChatGPT
Indonesia
adoption
risk
trust
X.com
Diffusion of Innovation theory (DoI)
Latent Dirichlet Allocation (LDA)
topic modeling
influential actors
Social Network Analysis (SNA)
sentiment analysis日期 2024 上傳時間 3-六月-2024 11:53:37 (UTC+8) 摘要 印度尼西亞擁有超過2億活躍的互聯網用戶，對ChatGPT來說是一個重要的市場。然而，在2023年初，由於其未註冊的狀態引起了有關監管的潛在禁令的討論。儘管學術界批評存在問題，如抄襲和网络安全风险，但對ChatGPT的興趣仍在增加。過去的研究強調了它的生產效益，但也提出了隱私和网络安全問題，尤其是在教育方面。值得注意的是，在印度尼西亞，有限的研究探討了社交媒體用戶如何討論ChatGPT的採用、風險和信任問題。本研究以創新擴散理論為指導，旨在揭示在線討論中有關ChatGPT的關鍵話題和情緒，以及確定塑造這些討論的有影響力的行為者。 本研究調查了印度尼西亞情境下的ChatGPT採用、風險和信任，重點關注X.com用戶之間的討論。它識別了在線討論中相關的話題和情緒。此外，它還檢查了由有影響力的行為者推動的討論。通過分析從ChatGPT推出到2024年1月31日的推文，本研究採用了使用Python的大數據分析方法。此方法包括時間序列和詞頻分析、潛在狄利克雷分配（LDA）主題建模、情感分析和社交網絡分析（SNA）。 從2022年11月30日到2024年1月31日，有9371條推文討論了ChatGPT的採用，1637條推文討論了ChatGPT的風險，以及950條推文涉及了ChatGPT的信任問題在X.com上。趨勢分析顯示了採用、風險和信任討論中的一致模式，顯著事件推動了興趣的波動。數據顯示2023年1月和2月的活動增加，從2023年6月到8月下降，並從2023年9月開始重新引起興趣，達到2023年12月的高峰。詞頻分析突出顯示了像"chatgpt"和"artificial intelligence"這樣的主要詞彙，而像"afraid"、"danger"和"threat"這樣的詞彙則表明了對風險的關注。此外，“trust”和“human”等反复提到的词语反映了对可靠性和道德影响的讨论，而像“work”、“write”和“search”这样的词语则暗示了在工作场所和内容生成中探索ChatGPT的作用。 LDA主題显示了对ChatGPT响应的探索和实验成为ChatGPT采用相关讨论中最相关的话题，大多数表达了积极的情绪。然而，在风险相关的讨论中，积极和负面情绪之间的极化是明显的，用户表达了对ChatGPT实际好处的赞赏，例如简化任务和提高生产力，但仍然对潜在的缺点持谨慎态度，包括工作岗位替换和对分析思维的影响。同样，与信任相关的讨论主要表现出积极的情绪，反映了用户对ChatGPT能力的信心，尽管对其响应中的不准确性和错误信息持续关注。 社交网络分析揭示了Twitter机器人（如@collegemenfess和@worksfess）等有影响力的行为者，它们在教育和专业背景中与用户进行互动。这些机器人作为匿名讨论的平台，引发了交互并塑造了有关ChatGPT采用的对话。此外，@collegemenfess在ChatGPT与风险相关的讨论中也具有影响力。令人惊讶的是，这些机器人已经积累了可观的关注度，表明它们在引导各自领域内的讨论中具有影响力。此外，像@ainunnajib和@gibran_tweet这样的个人用户在与信任相关的讨论中也成为有影响力的行为者，为有关ChatGPT可靠性和道德影响的讨论做出了贡献。进一步的研究发现，像@tanyakanrl、@tanyarlfes和@ismailfahmi这样的用户在网络内部建立联系并促进了关于ChatGPT的讨论。这些有影响力的行为者的存在突显了像“menfess”
Indonesia, with over 200 million active internet users, is a significant market for ChatGPT. However, in early 2023, concerns about its unregistered status led to discussions about potential bans for oversight. Despite criticisms from academics citing issues like plagiarism and cybersecurity risks, interest in ChatGPT has grown. Past studies highlighted its productivity benefits but also raised concerns about privacy and cybersecurity, especially in education. Notably, there's limited research on how social media users discuss ChatGPT adoption, risks, and trust, particularly in Indonesia. This study, guided by the diffusion of innovation theory, aims to uncover key topics and sentiments in online discussions about ChatGPT, as well as identify influential actors shaping these discussions. This study investigates the ChatGPT adoption, risk, and trust within the Indonesian context, focusing on discussions among X.com users. It identifies relevant topics and sentiments in online discussions. Additionally, it examines discussions driven by the influential actors. By analyzing tweets spanning from the launch of ChatGPT until January 31, 2024, the study employs a big data analytics approach using Python. This approach includes time series and word frequency analysis, Latent Dirichlet Allocation (LDA) topic modeling, sentiment analysis, and social network analysis (SNA). Between November 30, 2022, and January 31, 2024, there were 9,371 tweets discussing ChatGPT adoption, 1,637 tweets about ChatGPT risk, and 950 tweets regarding ChatGPT trust on X.com. Trend analysis shows consistent patterns in adoption, risk, and trust discussions, with notable events driving interest fluctuations. The data reveal increased activity in January and February 2023, a dip from June to August 2023, and renewed interest from September 2023 onwards, peaking in December 2023. Word frequency analysis highlights dominant terms like "chatgpt" and "artificial intelligence," with terms like "afraid," "danger," and "threat" indicating concerns about risks. Additionally, recurrent mentions of "trust" and "human" reflect discussions on reliability and ethical implications, while terms like "work," "write," and "search" suggest exploration of ChatGPT's role in the workplace and content generation. The LDA topic shows the exploration and experimentation with ChatGPT responses emerge as the most relevant topic discussed within the ChatGPT adoption related discussions, with positive sentiment mostly expressed. However, polarization between positive and negative sentiment is evident, particularly in risk-related discussions, where surprising positivity is found alongside anticipated concerns. Users express appreciation for ChatGPT's practical benefits, such as streamlining tasks and enhancing productivity, yet remain cautious about potential drawbacks, including job displacement and impacts on analytical thinking. Similarly, trust-related discussions predominantly exhibit positive sentiments, reflecting users' confidence in ChatGPT's capabilities despite persistent concerns about inaccuracies and misinformation within its responses. The social network analysis reveals Twitter bots such as @collegemenfess and @worksfess as influential actors, which engage users within the educational and professional contexts. These bots serve as platforms for anonymous discussions, triggering interactions and shaping conversations about ChatGPT adoption. Additionally, @collegemenfess is also influential in ChatGPT risk-related discussions. Surprisingly, these bots have amassed significant followings, indicating their influence in steering discussions within their domains. Additionally, individual users like @ainunnajib and @gibran_tweet emerge as influential actors in trust-related discussions, contributing to the discussions about ChatGPT's reliability and ethical implications. Further examination uncovers users like @tanyakanrl, @tanyarlfes and @ismailfahmi, bridging connections within the network and facilitating discussions about ChatGPT across various user groups. The presence of these influential actors highlights the importance of platforms like ‘menfess’ accounts in fostering open dialogue while preserving user anonymity.參考文獻 Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., Khatib, S. E., Rahal, M., Sahban, A., Abdelaziz, D. H., Mansour, N. O., AlZayer, R., Khalil, R., Fekih-Romdhane, F., Hallit, R., … Sallam, M. (2023). 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ACM Transactions on Knowledge Discovery from Data, 9(3), 1–26. https://doi.org/10.1145/2700398 Zhou, S., Kan, P., Huang, Q., & Silbernagel, J. (2023). A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura. Journal of Information Science, 49(2), 465-479. https://doi.org/10.1177/01655515211007724 Zou, W., Li, J., Yang, Y., & Tang, L. (2023). Exploring the early adoption of Open AI among laypeople and technical professionals: An analysis of Twitter conversations on #ChatGPT and #GPT3. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2023.2295725 描述 碩士
國立政治大學
國際傳播英語碩士學位學程(IMICS)
110461001資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110461001 資料類型 thesis dc.contributor.advisor 林翠絹 zh_TW dc.contributor.advisor Lin, Trisha Tsui-Chuan en_US dc.contributor.author (作者) 李勇俊 zh_TW dc.contributor.author (作者) Putra, Rio Oktora Nanda en_US dc.creator (作者) 李勇俊 zh_TW dc.creator (作者) Putra, Rio Oktora Nanda en_US dc.date (日期) 2024 en_US dc.date.accessioned 3-六月-2024 11:53:37 (UTC+8) - dc.date.available 3-六月-2024 11:53:37 (UTC+8) - dc.date.issued (上傳時間) 3-六月-2024 11:53:37 (UTC+8) - dc.identifier (其他 識別碼) G0110461001 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/151535 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際傳播英語碩士學位學程(IMICS) zh_TW dc.description (描述) 110461001 zh_TW dc.description.abstract (摘要) 印度尼西亞擁有超過2億活躍的互聯網用戶，對ChatGPT來說是一個重要的市場。然而，在2023年初，由於其未註冊的狀態引起了有關監管的潛在禁令的討論。儘管學術界批評存在問題，如抄襲和网络安全风险，但對ChatGPT的興趣仍在增加。過去的研究強調了它的生產效益，但也提出了隱私和网络安全問題，尤其是在教育方面。值得注意的是，在印度尼西亞，有限的研究探討了社交媒體用戶如何討論ChatGPT的採用、風險和信任問題。本研究以創新擴散理論為指導，旨在揭示在線討論中有關ChatGPT的關鍵話題和情緒，以及確定塑造這些討論的有影響力的行為者。 本研究調查了印度尼西亞情境下的ChatGPT採用、風險和信任，重點關注X.com用戶之間的討論。它識別了在線討論中相關的話題和情緒。此外，它還檢查了由有影響力的行為者推動的討論。通過分析從ChatGPT推出到2024年1月31日的推文，本研究採用了使用Python的大數據分析方法。此方法包括時間序列和詞頻分析、潛在狄利克雷分配（LDA）主題建模、情感分析和社交網絡分析（SNA）。 從2022年11月30日到2024年1月31日，有9371條推文討論了ChatGPT的採用，1637條推文討論了ChatGPT的風險，以及950條推文涉及了ChatGPT的信任問題在X.com上。趨勢分析顯示了採用、風險和信任討論中的一致模式，顯著事件推動了興趣的波動。數據顯示2023年1月和2月的活動增加，從2023年6月到8月下降，並從2023年9月開始重新引起興趣，達到2023年12月的高峰。詞頻分析突出顯示了像"chatgpt"和"artificial intelligence"這樣的主要詞彙，而像"afraid"、"danger"和"threat"這樣的詞彙則表明了對風險的關注。此外，“trust”和“human”等反复提到的词语反映了对可靠性和道德影响的讨论，而像“work”、“write”和“search”这样的词语则暗示了在工作场所和内容生成中探索ChatGPT的作用。 LDA主題显示了对ChatGPT响应的探索和实验成为ChatGPT采用相关讨论中最相关的话题，大多数表达了积极的情绪。然而，在风险相关的讨论中，积极和负面情绪之间的极化是明显的，用户表达了对ChatGPT实际好处的赞赏，例如简化任务和提高生产力，但仍然对潜在的缺点持谨慎态度，包括工作岗位替换和对分析思维的影响。同样，与信任相关的讨论主要表现出积极的情绪，反映了用户对ChatGPT能力的信心，尽管对其响应中的不准确性和错误信息持续关注。 社交网络分析揭示了Twitter机器人（如@collegemenfess和@worksfess）等有影响力的行为者，它们在教育和专业背景中与用户进行互动。这些机器人作为匿名讨论的平台，引发了交互并塑造了有关ChatGPT采用的对话。此外，@collegemenfess在ChatGPT与风险相关的讨论中也具有影响力。令人惊讶的是，这些机器人已经积累了可观的关注度，表明它们在引导各自领域内的讨论中具有影响力。此外，像@ainunnajib和@gibran_tweet这样的个人用户在与信任相关的讨论中也成为有影响力的行为者，为有关ChatGPT可靠性和道德影响的讨论做出了贡献。进一步的研究发现，像@tanyakanrl、@tanyarlfes和@ismailfahmi这样的用户在网络内部建立联系并促进了关于ChatGPT的讨论。这些有影响力的行为者的存在突显了像“menfess” zh_TW dc.description.abstract (摘要) Indonesia, with over 200 million active internet users, is a significant market for ChatGPT. However, in early 2023, concerns about its unregistered status led to discussions about potential bans for oversight. Despite criticisms from academics citing issues like plagiarism and cybersecurity risks, interest in ChatGPT has grown. Past studies highlighted its productivity benefits but also raised concerns about privacy and cybersecurity, especially in education. Notably, there's limited research on how social media users discuss ChatGPT adoption, risks, and trust, particularly in Indonesia. This study, guided by the diffusion of innovation theory, aims to uncover key topics and sentiments in online discussions about ChatGPT, as well as identify influential actors shaping these discussions. This study investigates the ChatGPT adoption, risk, and trust within the Indonesian context, focusing on discussions among X.com users. It identifies relevant topics and sentiments in online discussions. Additionally, it examines discussions driven by the influential actors. By analyzing tweets spanning from the launch of ChatGPT until January 31, 2024, the study employs a big data analytics approach using Python. This approach includes time series and word frequency analysis, Latent Dirichlet Allocation (LDA) topic modeling, sentiment analysis, and social network analysis (SNA). Between November 30, 2022, and January 31, 2024, there were 9,371 tweets discussing ChatGPT adoption, 1,637 tweets about ChatGPT risk, and 950 tweets regarding ChatGPT trust on X.com. Trend analysis shows consistent patterns in adoption, risk, and trust discussions, with notable events driving interest fluctuations. The data reveal increased activity in January and February 2023, a dip from June to August 2023, and renewed interest from September 2023 onwards, peaking in December 2023. Word frequency analysis highlights dominant terms like "chatgpt" and "artificial intelligence," with terms like "afraid," "danger," and "threat" indicating concerns about risks. Additionally, recurrent mentions of "trust" and "human" reflect discussions on reliability and ethical implications, while terms like "work," "write," and "search" suggest exploration of ChatGPT's role in the workplace and content generation. The LDA topic shows the exploration and experimentation with ChatGPT responses emerge as the most relevant topic discussed within the ChatGPT adoption related discussions, with positive sentiment mostly expressed. However, polarization between positive and negative sentiment is evident, particularly in risk-related discussions, where surprising positivity is found alongside anticipated concerns. Users express appreciation for ChatGPT's practical benefits, such as streamlining tasks and enhancing productivity, yet remain cautious about potential drawbacks, including job displacement and impacts on analytical thinking. Similarly, trust-related discussions predominantly exhibit positive sentiments, reflecting users' confidence in ChatGPT's capabilities despite persistent concerns about inaccuracies and misinformation within its responses. The social network analysis reveals Twitter bots such as @collegemenfess and @worksfess as influential actors, which engage users within the educational and professional contexts. These bots serve as platforms for anonymous discussions, triggering interactions and shaping conversations about ChatGPT adoption. Additionally, @collegemenfess is also influential in ChatGPT risk-related discussions. Surprisingly, these bots have amassed significant followings, indicating their influence in steering discussions within their domains. Additionally, individual users like @ainunnajib and @gibran_tweet emerge as influential actors in trust-related discussions, contributing to the discussions about ChatGPT's reliability and ethical implications. Further examination uncovers users like @tanyakanrl, @tanyarlfes and @ismailfahmi, bridging connections within the network and facilitating discussions about ChatGPT across various user groups. The presence of these influential actors highlights the importance of platforms like ‘menfess’ accounts in fostering open dialogue while preserving user anonymity. en_US dc.description.tableofcontents Acknowledgement 3 Abstract 4 Table of Contents 6 List of Table and Figure 9 Chapter 1. INTRODUCTION 10 1.1. Research Background 10 1.2. Research Purpose 13 1.3. Research Significance and Expected Contributions 14 Chapter 2. LITERATURE REVIEW 15 2.1. The Use and Issues Regarding ChatGPT in Indonesia 15 2.2. Diffusion of Innovation Theory 18 2.2.1. ChatGPT Adoption 19 2.2.2. Opinion Leaders’ Role in Shaping Online Discussions 22 2.3. Risk Concerns and Trust in ChatGPT 23 2.3.1. Risk Concerns in ChatGPT Adoption 24 2.3.2. Trust in ChatGPT 28 2.4. Big Data Analytics Study on ChatGPT 30 Chapter 3. METHOD 33 3.1. Data Crawling 35 3.2. Data Preprocessing 36 Data Cleaning and Case Folding 36 Tokenizing, Stemming/Lemmatization, and Stop Words 37 3.3. Data Analysis 39 3.3.1. Time Series and Word Frequency Analysis 39 3.3.2. Latent Dirichlet Allocation (LDA) Topic Modeling 39 3.3.3. Sentiment Analysis 43 3.3.4. Social Network Analysis (SNA) 46 Chapter 4. FINDINGS 48 4.1. ChatGPT Adoption, Risk, and Trust Online Discussions Trend 48 November 2022 - January 2023: Emergence and Initial Growth 49 February 2023 - April 2023: Significant Increase and Active Academic Discussion 49 May 2023 - August 2023: Fluctuations and Low Interest 50 September 2023 - December 2023: Renewed Interest 51 January 2024: Rising Focus on Trust 52 Comparative Trends 52 Identified Terms Frequently Appearing in the Discussions 54 4.2. ChatGPT Adoption 58 4.2.1. Topic Discussed and Sentiment within ChatGPT Adoption Discussions 58 4.2.2. Influential Actors within ChatGPT Adoption Discussions and Their Sentiment 63 Discussion Network Structure 64 Centrality Metrics and Sentiment Analysis of Influential Actors' Discussions 66 4.3. ChatGPT Risk 69 4.3.1. Sentiment and Relevant Topics within ChatGPT Risk-related Discussions 69 Topics Discussed in ChatGPT and Risk Discussions 71 Topics Discussed in ChatGPT and Risk Discussions with Positive Sentiment 72 Topics Discussed in ChatGPT and Risk Discussions with Negative Sentiment 73 4.3.2. Influential Actors within Discussions Regarding ChatGPT Risk 76 Discussion Network Structure 76 Centrality Metrics and Sentiment Analysis of Influential Actors' Discussions 78 4.4. ChatGPT and Trust 81 4.4.1. Sentiment and Relevant Topics within ChatGPT and Trust-related Discussions 81 Topics Discussed in ChatGPT and Trust Discussions 82 Topics Discussed in ChatGPT and Trust Discussions with Positive Sentiment 82 Topics Discussed in ChatGPT and Trust Discussions with Negative Sentiment 85 4.4.2. Influential Actors within ChatGPT and Trust-related Discussions 87 Discussion Network Structure 88 Centrality Metrics and Sentiment Analysis of Influential Actors' Discussions 90 Chapter 5. DISCUSSION & CONCLUSION 93 5.1. Summary 93 5.2. Trialability and Relative Advantage in ChatGPT Adoption, Risk, and Trust 94 5.2.1. ChatGPT’s Trialability and Relative Advantage Aspects Influence Risk & Trust Sentiments 94 5.2.2. Further Exploration of How ChatGPT’s Relative Advantage Influence Risk and Trust 97 5.3. Challenges and Limitation in Sentiment Analysis: Language Complexity and Satirical Expression 99 5.4. Influential Actors and Menfess Accounts Driving Network Discussions 101 Bots are Among the Identified Influential Actors 101 Analyzing ‘Menfess’ Accounts and Impact on Discussions 102 5.5. Contribution, Limitation and Future Study Direction 106 5.5.1. Research Contribution 106 5.5.2. Limitation & Future Studies Direction 107 References 109 Appendix A. Summary of Journal Articles About ChatGPT Risk or Trust 124 Appendix B. Summary of journal articles about ChatGPT using big data analytics 131 Appendix C. Original LDA Results and Tweet Sample 137 Appendix D. Python Code 143 zh_TW dc.format.extent 6650633 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110461001 en_US dc.subject (關鍵詞) ChatGPT zh_TW dc.subject (關鍵詞) X.com zh_TW dc.subject (關鍵詞) 印尼 zh_TW dc.subject (關鍵詞) ChatGPT en_US dc.subject (關鍵詞) Indonesia en_US dc.subject (關鍵詞) adoption en_US dc.subject (關鍵詞) risk en_US dc.subject (關鍵詞) trust en_US dc.subject (關鍵詞) X.com en_US dc.subject (關鍵詞) Diffusion of Innovation theory (DoI) en_US dc.subject (關鍵詞) Latent Dirichlet Allocation (LDA) en_US dc.subject (關鍵詞) topic modeling en_US dc.subject (關鍵詞) influential actors en_US dc.subject (關鍵詞) Social Network Analysis (SNA) en_US dc.subject (關鍵詞) sentiment analysis en_US dc.title (題名) 檢視印尼ChatGPT採用、風險和信任議題之網路討論、 具影響力者及情緒：大數據分析觀點 zh_TW dc.title (題名) Examining online discussions, influential actors, and sentiments of ChatGPT adoption, risk and trust in Indonesia: A big data analytics perspective en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. 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