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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 謝吉隆 | zh_TW |
| dc.contributor.advisor | Ji-Lung Hsieh | en |
| dc.contributor.author | 章璟 | zh_TW |
| dc.contributor.author | Chong Jing | en |
| dc.date.accessioned | 2025-02-27T16:09:57Z | - |
| dc.date.available | 2025-02-28 | - |
| dc.date.copyright | 2025-02-27 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-02-12 | - |
| dc.identifier.citation | Airenti, G. (2015). The Cognitive Bases of Anthropomorphism: From Relatedness to Empathy. International Journal of Social Robotics, 7(1), 117–127. https://doi.org/10.1007/s12369-014-0263-x
Cave, S., Craig, C., Dihal, K., Dillon, S., Montgomery, J., Singler, B., & Taylor, L. (2018). Portrayals and perceptions of AI and why they matter. https://doi.org/10.17863/CAM.34502 Cheng, M., Gligoric, K., Piccardi, T., & Jurafsky, D. (2024). AnthroScore: A Computational Linguistic Measure of Anthropomorphism (arXiv:2402.02056). arXiv. https://doi.org/10.48550/arXiv.2402.02056 Darling, K. (2015). “Who’s Johnny?” Anthropomorphic Framing in Human-Robot Interaction, Integration, and Policy (SSRN Scholarly Paper 2588669). Social Science Research Network. https://doi.org/10.2139/ssrn.2588669 Deshpande, A., Rajpurohit, T., Narasimhan, K., & Kalyan, A. (2023). Anthropomorphization of AI: Opportunities and Risks (arXiv:2305.14784). arXiv. https://doi.org/10.48550/arXiv.2305.14784 Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886. https://doi.org/10.1037/0033-295X.114.4.864 Gros, D., Li, Y., & Yu, Z. (2022). Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 3266–3284). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.215 Li J., Wang S., Chen X., & Wang D. (2021). Research on Element Extraction of Personified Sentences Based on Enhanced Characters. In Li S., Sun M., Liu Y., Wu H., Liu K., Che W., He S., & Rao G. (Eds.), Proceedings of the 20th Chinese National Conference on Computational Linguistics (pp. 612–621). Chinese Information Processing Society of China. https://aclanthology.org/2021.ccl-1.55/ Mendelsohn, J., Tsvetkov, Y., & Jurafsky, D. (2020). A Framework for the Computational Linguistic Analysis of Dehumanization. Frontiers in Artificial Intelligence, 3, 55. https://doi.org/10.3389/frai.2020.00055 Newman, N., Fletcher, R., Robertson, C. T., Ross Arguedas, A., & Nielsen, R. K. (2024). Reuters Institute digital news report 2024. Reuters Institute for the Study of Journalism. https://doi.org/10.60625/RISJ-VY6N-4V57 Placani, A. (2024). Anthropomorphism in AI: Hype and fallacy. AI and Ethics, 4(3), 691–698. https://doi.org/10.1007/s43681-024-00419-4 Proudfoot, D. (2011). Anthropomorphism and AI: Turingʼs much misunderstood imitation game. Artificial Intelligence, 175(5), 950–957. https://doi.org/10.1016/j.artint.2011.01.006 Salles, A., Evers, K., & Farisco, M. (2020). Anthropomorphism in AI. AJOB Neuroscience, 11(2), 88–95. https://doi.org/10.1080/21507740.2020.1740350 Shanahan, M. (2023). Talking About Large Language Models (arXiv:2212.03551). arXiv. https://doi.org/10.48550/arXiv.2212.03551 Shneiderman, B. (2020). Design Lessons From AI’s Two Grand Goals: Human Emulation and Useful Applications. IEEE Transactions on Technology and Society, 1(2), 73–82. IEEE Transactions on Technology and Society. https://doi.org/10.1109/TTS.2020.2992669 Stahl, W. A. (1995). Venerating the Black Box: Magic in Media Discourse on Technology. Science, Technology, & Human Values, 20(2), 234–258. https://doi.org/10.1177/016224399502000205 Tipler, C., & Ruscher, J. B. (2014). Agency’s Role in Dehumanization: Non-human Metaphors of Out-groups. Social and Personality Psychology Compass, 8(5), 214–228. https://doi.org/10.1111/spc3.12100 Watson, D. (2019). The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence. Minds and Machines, 29(3), 417–440. https://doi.org/10.1007/s11023-019-09506-6 Waytz, A., Cacioppo, J., & Epley, N. (2010). Who Sees Human?: The Stability and Importance of Individual Differences in Anthropomorphism. Perspectives on Psychological Science, 5(3), 219–232. https://doi.org/10.1177/1745691610369336 Wortham, R. H., & Theodorou, A. (2017). Robot transparency, trust and utility. Connection Science, 29(3), 242–248. https://doi.org/10.1080/09540091.2017.1313816 Yanai, I., & Lercher, M. (2020). The two languages of science. Genome Biology, 21(1), 147. https://doi.org/10.1186/s13059-020-02057-5 Zhong, R., & Ma, M. (2022). Effects of communication style, anthropomorphic setting and individual differences on older adults using voice assistants in a health context. BMC Geriatrics, 22(1), 751. https://doi.org/10.1186/s12877-022-03428-2 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97094 | - |
| dc.description.abstract | 近幾年人工智慧（AI）發展迅速，為了讓社會大眾更容易理解這項前瞻性的技術，媒體經常採用擬人化的方式來報導AI。然而，過度將人類特質投射到AI身上，可能導致公眾對AI的實際能力產生誤解，進而影響民眾對相關科技風險與倫理議題的判斷。因此，本研究選擇台灣主要數位新聞平台ETtoday作為研究案例，分析其AI相關報導中的擬人化現象。
研究採用史丹佛大學自然語言處理團隊開發的AnthroScore框架，並針對中文AI內容特性進行了三項重要改進：(1) 採用兩步驟預測法，讓模型在評估前充分理解完整語境；(2) 調整為中文適用的代詞列表，如「他」、「她」、「它」；(3) 引入基於閾值的過濾機制，提高結果可靠性。 本論文分析2016年至2024年間的AI新聞，發現三個重要趨勢：第一，AI新聞的擬人化程度呈現顯著上升趨勢；第二，娛樂、社會及體育新聞相較其他類別展現較高的擬人化分數,反映這些領域更傾向將AI描述為具有人性特質;第三，ChatGPT相關報導比一般AI實體有更高的擬人化程度，顯示對話式AI更容易使用擬人化描述。 本研究成功將AnthroScore框架應用於中文語境，為未來跨語言的擬人化研究提供了新的研究途徑。本論文結果揭示了台灣媒體對AI的擬人化趨勢，提醒新聞從業人員在報導AI議題時應更加謹慎，以避免誤導讀者。本研究結果不僅有助於了解AI在華語媒體中的描繪方式，更對推動負責任的科技新聞報導產生深遠的影響。 | zh_TW |
| dc.description.abstract | With the rapid advancement of artificial intelligence (AI), news media often employ anthropomorphic representations to help the public better understand this emerging technology. However, excessive attribution of human characteristics to AI can lead to misconceptions about its actual capabilities and affect public perception of technological risks and ethical issues. This study examines AI-related news from ETtoday, a major Taiwanese digital news platform, to explore anthropomorphism in AI reporting.
Applying the AnthroScore framework, developed by Stanford’s NLP Group, this study introduces three key modifications for Chinese-language analysis: (1) a two-step prediction method to enhance contextual understanding, (2) an adapted pronoun list including "他" (he), "她" (she), and "它" (it), and (3) a threshold-based filtering mechanism for improved reliability. Analyzing AI-related news articles published between 2016 and 2024, this study finds: (1) a significant increase in AI anthropomorphism over time, (2) higher anthropomorphism in entertainment, society, and sports news, and (3) ChatGPT-related articles showing greater anthropomorphism than general AI reports, indicating conversational AI elicits stronger human-like descriptions. This study contributes by adapting AnthroScore for Chinese, offering insights for cross-linguistic research. It also highlights Taiwan’s media tendency to anthropomorphize AI, underscoring the need for responsible journalism to prevent public misinterpretation. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-02-27T16:09:57Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-02-27T16:09:57Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1 Background and Motivation 1 Chapter 2 Literature Review 3 2.1 Anthropomorphism 3 2.2 Harms and Benefits of Anthropomorphism 4 2.3 Anthropomorphism in AI 6 2.4 Review of AnthroScore Method 7 2.4.1 Technical Implementation 8 Chapter 3 Data and Methods 11 3.1 Datasets 11 3.1.1 Data Filtering and Preprocessing 12 3.2 Adaptation of AnthroScore to Chinese 15 3.2.1 Two-Pass Contextual Prediction 16 3.2.2 Chinese-Specific Pronoun Adaptation 16 3.2.3 Masking Entities and Threshold Filtering 18 3.2.4 Computing A score for each sentence 21 3.3 Correlation with Human Perception 22 Chapter 4 Experimental Results 25 4.1 Temporal Analysis: Rising Anthropomorphic Patterns in ETtoday News. 25 4.2 Category Analysis: Higher Anthropomorphism in Entertainment, Society, and Sports News. 26 4.3 Entity Analysis: Higher Anthropomorphism in ChatGPT-Related Content. 31 4.4 Verb Usage Analysis: Distinguishing High- and Low-Scoring Sentences. 32 Chapter 5 Conclusion 35 5.1 Limitations and Future Work 36 References 38 Appendix A: Threshold Filtering Results by Factor 41 | - |
| dc.language.iso | en | - |
| dc.subject | 新聞分析 | zh_TW |
| dc.subject | 中文媒體 | zh_TW |
| dc.subject | 人工智慧 | zh_TW |
| dc.subject | 計算語言學 | zh_TW |
| dc.subject | 擬人化 | zh_TW |
| dc.subject | Anthroscore | zh_TW |
| dc.subject | Computational Linguistics | en |
| dc.subject | AnthroScore | en |
| dc.subject | Anthropomorphism | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject | News Analysis | en |
| dc.subject | Chinese Media | en |
| dc.title | 中文新聞語境中的擬人化分析：以人工智慧論述為例 | zh_TW |
| dc.title | Anthropomorphic Analysis of Chinese News: A Case Study on Artificial Intelligence Discourse | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 謝舒凱;黃瀚萱 | zh_TW |
| dc.contributor.oralexamcommittee | Shu-Kai Hsieh;Hen-Hsen Huang | en |
| dc.subject.keyword | Anthroscore,擬人化,人工智慧,新聞分析,中文媒體,計算語言學, | zh_TW |
| dc.subject.keyword | AnthroScore,Anthropomorphism,Artificial Intelligence,News Analysis,Chinese Media,Computational Linguistics, | en |
| dc.relation.page | 42 | - |
| dc.identifier.doi | 10.6342/NTU202500657 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-02-13 | - |
| dc.contributor.author-college | 社會科學院 | - |
| dc.contributor.author-dept | 新聞研究所 | - |
| dc.date.embargo-lift | 2025-02-28 | - |
| 顯示於系所單位： | 新聞研究所 | |
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