Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/136840| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 梁定澎<br>彭志宏 | zh_TW |
| dc.contributor.advisor | Liang, Ting-Peng<br>Peng, Chih-Hung | en_US |
| dc.contributor.author | 郭庭瑋 | zh_TW |
| dc.contributor.author | Kuo, Ting-Wei | en_US |
| dc.creator | 郭庭瑋 | zh_TW |
| dc.creator | Kuo, Ting-Wei | en_US |
| dc.date | 2021 | en_US |
| dc.date.accessioned | 2021-09-02T07:48:47Z | - |
| dc.date.available | 2021-09-02T07:48:47Z | - |
| dc.date.issued | 2021-09-02T07:48:47Z | - |
| dc.identifier | G0108356001 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/136840 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 資訊管理學系 | zh_TW |
| dc.description | 108356001 | zh_TW |
| dc.description.abstract | 假新聞(fake news)一詞在2016年美國總統大選後被大家廣為使用，人們自從劍橋分析事件(Cambridge Analytica)之後開始逐漸重視假新聞所帶來的危害，2019年底遭逢COVID-19開始在全球肆虐，關於此疾病的假新聞大量的出現在各個媒體上，也使得假新聞進入了另一個高峰期。\n假新聞的研究主題有很多，目前僅有部分學者針對特定的主題做小範圍的分析，尚未有學者針對所有類型的文獻一個較有統整性的整理，因此本研究透過蒐集在Web of Science上蒐集假新聞的相關文獻，並透過書目計量學分析(Bibliometric Analysis)，去探討這些文獻，本研究以書目計量的輔助軟體(VOSviewer)完成相關的分析，透過作者給予文獻的關鍵字去了解目前的研究趨勢，並且將這些結果可視化，本研究也將蒐集而來的文獻進行學門的分群，對於不同學門的文獻內容做了進一步的分析，在最後也提出了了解假新聞研究領域閱讀文獻的推薦順序，供後續的研究人員作參考。 | zh_TW |
| dc.description.abstract | The phrase “fake news” has become popular in the wake of the United States presidential election of 2016. People became concerned about the dangers that could be caused by fake news ever since the Cambridge Analytica scandal. During the COVID-19 outbreak at the end of 2019, fake news about the disease rapidly spread across various media outlets, leading to a new peak in fake news.\nThere are many studies on fake news. In the current studies, researchers have only analyzed certain topics on a small scale, so this study set out to analyze all types of sources. This study collected studies about fake news from a website called Web of Science (WOS), and this study used bibliometric analysis to analyze the research. This study used a software called VOSviewer to help us complete the bibliometric analysis, recognize the research trends via author keywords, and visualize the results. This study also sorted the studies by research areas and analyzed them. Lastly, this study proposed a reading sequence for the studies for future researchers. | en_US |
| dc.description.tableofcontents | 謝辭 II\n摘要 III\nAbstract IV\n目次 I\n表次 IV\n圖次 V\n第一章 緒論 1\n第一節 研究背景 1\n第二節 研究動機 2\n第三節 研究目的 2\n第四節 研究流程 3\n第二章 文獻回顧 5\n第一節 什麼是假新聞 5\n第二節 社群媒體上的假新聞 7\n第三節 如何辨別假新聞 7\n第四節 書目計量學 9\n一、 書目計量學的基本概念 10\n二、 書目計量學的量化方法 10\n三、 書目計量學的輔助軟體 12\n四、 書目計量學的相關採用文獻 13\n第三章 研究方法 14\n第一節 採用研究工具 14\n一、 文獻資料庫平台 14\n二、 書目計量學輔助分析軟體 14\n第二節 選擇研究項目 15\n一、 假新聞的研究主題及研究趨勢 15\n二、 假新聞文獻的研究主體 15\n第三節 選擇分析方式 16\n第四節 文獻資料及檢索策略 17\n一、 關鍵字選擇 17\n二、 年份選擇 17\n第四章 研究趨勢與書目計量結果 18\n第一節 假新聞文獻的發表趨勢 18\n第二節 作者關鍵字分析 19\n一、 SOCIOLOGY(社會學) 28\n二、 COMPUTER SCIENCE INFORMATION SYSTEMS(電腦科學與資訊系統) 33\n三、 POLITICAL SCIENCE(政治科學) 39\n四、 INFORMATION SCIENCE LIBRARY SCIENCE(資訊科學與圖書館科學) 46\n五、 COMMUNICATION(傳播學) 56\n六、 五個學門之間相互引用的情形 69\n七、 作者關鍵字的年份變化 70\n第三節 文獻被引用分析 75\n第四節 共同作者分析 78\n第五節 文獻書目耦合分析 80\n第六節 文獻共被引用分析 83\n第五章 結論與建議 87\n第一節 研究結論 87\n一、 假新聞的研究主題 87\n二、 假新聞的研究趨勢 90\n第二節 研究貢獻 91\n一、 理論層面貢獻 91\n二、 實務層面貢獻 92\n第三節 研究限制 92\n第四節 未來方向 92\n參考文獻 94\n附錄:書目計量學分析資料 98 | zh_TW |
| dc.format.extent | 5837044 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0108356001 | en_US |
| dc.subject | 假新聞 | zh_TW |
| dc.subject | 書目計量學 | zh_TW |
| dc.subject | 系統性分析 | zh_TW |
| dc.subject | 趨勢 | zh_TW |
| dc.subject | 學門 | zh_TW |
| dc.subject | Fake News | en_US |
| dc.subject | Bibliometric Analysis | en_US |
| dc.subject | System Analysis | en_US |
| dc.subject | Trends | en_US |
| dc.subject | Discipline | en_US |
| dc.title | 假新聞研究趨勢及書目計量學分析 | zh_TW |
| dc.title | Fake News: Research Trends and A Bibliometric Analysis | en_US |
| dc.type | thesis | en_US |
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CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377.\nConstine, J., & Hatmaker, T. (2018). Facebook admits Cambridge Analytica hijacked data on up to 87 m users. TechCrunch. Retrieved from https://techcrunch.com/2018/04/04/cambridge-analytica-87-million/\nCorner, J. (2017). Fake news, post-truth and media–political change. Media, Culture & Society, 39(7), 1100–1107.\nEdson C. Tandoc Jr., Zheng Wei Lim & Richard Ling (2018) Defining “Fake News”, Digital Journalism, 6:2, 137-153, DOI: 10.1080/21670811.2017.1360143\nGhanem, B., Rosso, P., & Rangel, F. (2020). An Emotional Analysis of False Information in Social Media and News Articles [Article]. Acm Transactions on Internet Technology, 20(2), 18, Article 19. https://doi.org/10.1145/3381750\nGrinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). 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J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.\nvan Eck, N. J., Waltman, L., Dekker, R., & van den Berg, J. (2010). A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. Journal of the American Society for Information Science and Technology, 61(12), 2405-2416.\nvan Eck, N. J., Waltman, L., Noyons, E. C. M., & Buter, R. K. (2010). Automatic term identification for bibliometric mapping. Scientometrics, 82(3), 581-596.\nWang, M., Rao, M. K., & Sun, Z. P. Typology, Etiology, and Fact-Checking: A Pathological Study of Top Fake News in China [Article; Early Access]. Journalism Practice, 19. https://doi.org/10.1080/17512786.2020.1806723\nWang, Y. X., McKee, M., Torbica, A., & Stuckler, D. (2019). Systematic Literature Review on the Spread of Health-related Misinformation on Social Media [Review]. Social Science & Medicine, 240, 12, Article 112552. https://doi.org/10.1016/j.socscimed.2019.112552\nWardle, C. (2017). “Fake News.” It’s Complicated. https://medium.com/1st-draft/fake-newsits-complicated-d0f773766c79.\nZupic, I., & Čater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429–472. | zh_TW |
| dc.identifier.doi | 10.6814/NCCU202101434 | en_US |
| item.openairetype | thesis | - |
| item.fulltext | With Fulltext | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.grantfulltext | embargo_20260819 | - |
| item.cerifentitytype | Publications | - |
| Appears in Collections: | 學位論文 | |
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