請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77007完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳家麟(Chia-Lin Chen) | |
| dc.contributor.author | Yi-Pai Chang | en |
| dc.contributor.author | 張以白 | zh_TW |
| dc.date.accessioned | 2021-07-10T21:43:09Z | - |
| dc.date.available | 2021-07-10T21:43:09Z | - |
| dc.date.copyright | 2020-07-31 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-26 | |
| dc.identifier.citation | Louis A Fourt and Joseph W Woodlock. Early prediction of market success for new grocery products. Journal of marketing, 25(2):31–38, 1960. Edwin Mansfield. Technical change and the rate of imitation. Econometrica: Jour- nal of the Econometric Society, pages 741–766, 1961. Frank M Bass. A new product growth for model consumer durables. Management science, 15(5):215–227, 1969. John A Norton and Frank M Bass. A diffusion theory model of adoption and substi- tution for successive generations of high-technology products. Management science, 33(9):1069–1086, 1987. Louis P Bucklin and Sanjit Sengupta. The co-diffusion of complementary inno- vations: Supermarket scanners and upc symbols. Journal of Product Innovation Management, 10(2):148–160, 1993. Peter M Sandman. Responding to community outrage: Strategies for effective risk communication. AIHA, 1993. Kristen Alley Swain. Outrage factors and explanations in news coverage of the anthrax attacks. Journalism mass communication quarterly, 84(2):335–352, 2007. 曾薏珊. H1n1 新型流感報導中憤怨恐慌的要素與風險解釋. 政治大學新聞研 究所學位論文, pages 1–215, 2011. 王軍. 我省村民對 sars 的認知, 心理, 行為及相關關係的查與分析. Master’s thesis, 山西醫科大學, 2005. 謝曉非, 鄭蕊, 謝冬梅, 王惠, et al. Sars 中的心理恐慌現象分析. 2005. 李季梅, 陳寧, 陳安, 武豔南, et al. 突發事件的網絡輿情監測與恐慌度量系統. PhD thesis, 2009. 李偉, 李燕, and 江其生. 突發公衛事件輿情監測與恐慌度分析系的設計. 醫 療衛生裝備, (1):37–38, 2010. Nic Newman, Richard Fletcher, Antonis Kalogeropoulos, and Rasmus Nielsen. Reuters Institute digital news report 2019, volume 2019. Reuters Institute for the Study of Journalism, 2019. Melanie Mitchell. Complexity: A guided tour. Oxford University Press, 2009. 西瓜數據微信公眾號排行榜. https://data.xiguaji.com/. 微小寶微信公眾號排行榜. https://data.wxb.com/rank. James Holland Jones. Notes on R0. California: Department of Anthropological Sciences, 323:1–19, 2007. William Ogilvy Kermack and Anderson G McKendrick. A contribution to the math- ematical theory of epidemics. Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character, 115(772):700–721, 1927. Qun Li, Xuhua Guan, Peng Wu, Xiaoye Wang, Lei Zhou, Yeqing Tong, Ruiqi Ren, Kathy SM Leung, Eric HY Lau, Jessica Y Wong, et al. Early transmission dynamics in wuhan, china, of novel coronavirus–infected pneumonia. New England Journal of Medicine, 2020. Geert Hofstede and Michael H Bond. Hofstede’s culture dimensions: An indepen- dent validation using rokeach’s value survey. Journal of cross-cultural psychology, 15(4):417–433, 1984. Coronavirus (covid-19) situation and case numbers of australia. https:// www.health.gov.au/news/health-alerts/novel-coronavirus- 2019-ncov-health-alert/coronavirus-covid-19-current- situation-and-case-numbers. Scott L Althaus and David Tewksbury. Patterns of internet and traditional news media use in a networked community. Political communication, 17(1):21–45, 2000. 張雷. 論網絡政治謠言及其社會控制. 政治學研究, (2):52–59, 2007. 周曉虹. 風險社會中的謠言, 流言與恐慌. 南京醫科大學學報: 社會科學版, 11(6):413–417, 2011. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77007 | - |
| dc.description.abstract | 新冠肺炎在二O二O年帶給全人類莫大影響,全球破千萬人染疫,傳染速度非常之快,而新聞媒體的聲量隨著疫情發展一同成長,人民恐慌隨之而來,本研究欲以擴散模型與基礎再生數探討疫情期間的疫情擴散狀況與恐慌蔓延情況。 疫情方面,本研究將美國、英國、澳洲、加拿大、臺灣及中國的疫情資料作為輸入,利用巴斯擴散模型分析各國疫情擴散情形,解析出有別於疫情基礎再生數能夠給予的資訊,探討造成疫情以不同步調蔓延的原因。 恐慌方面,藉由風險溝通理論中的憤怨恐慌要素,本研究將恐慌要素轉化為關鍵字,從疫情期間各大媒體發布的新聞篩選出恐慌聲量,利用巴斯擴散模型進行分析,藉此衡量民眾恐慌程度。研究發現臺灣與中國在估計結果中與其他國家明顯不同,本研究認為可能與文化差異與手機使用情況有關。 最後,欲研究疫情與恐慌聲量之間的關係,以單向影響擴散模型分析,尋找疫情與恐慌聲量之相關性是否存在。研究發現中國的疫情對於恐慌聲量有顯著負向影響,推論可能因其政體與處理公衛事件之歷史有關;其餘各國資料皆不存在顯著單向影響性,應視疫情與恐慌為兩獨立成長的曲線。而不論是否存在關聯性,在研究中也發現各國的恐慌皆早於疫情開始蔓延。 | zh_TW |
| dc.description.abstract | COVID-19 has a great impact on all mankind in 2020. Millions of people around the world were infected. The media coverage of COVID-19 had grown together with the pandemic and the panic had followed. This study intended to make use of diffusion models and basic production number to explore the spread of pandemic and panic during the epidemic. To analyze the spread of COVID-19 in various countries, we used the Bass diffusion model and took the epidemic data of the United States, Britain, Australia, Canada, Taiwan and China as input to discuss the reason why spreads of the epidemic across countries were at different speed and extract information different from ${R_0}$ from estimates. As for panic, through the components of outrage proposed by Sandman in his risk communication theory, this study turned the components into keywords to quantify the amount of panic from the news released by major media during the epidemic and used the Bass diffusion model for analysis and to measure the degree of panic among the people. We found that Taiwan and China are apparently different from other countries in the estimation results. This study suggested that the underlying reasons behind the numbers by culture differences and difference of the usage rate of mobile phone. Lastly, to study the relationship between the epidemic and the panic, we used the one-way effect diffusion model to find out whether the correlation between the epidemic and the degree of panic exists. We found that the epidemic of China has a significant negative effect on panic, which might be caused by its regime. In other countries, the epidemic and panic should be regarded as two independent growth curves. Regardless of whether there is a correlation or not, we also found that the panic always started to spread out earlier than the epidemic. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-10T21:43:09Z (GMT). No. of bitstreams: 1 U0001-2607202010081500.pdf: 1912869 bytes, checksum: 6b0086a89d1d5096fabf46c42044e575 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書..................................... i 致謝............................................. i 中文摘要.......................................... ii Abstract........................................... iii 目錄............................................. iv 圖目錄............................................ v 表目錄............................................ vi 第一章 緒論....................................... 1 第二章 文獻回顧..................................... 3 2.1 擴散模型相關文獻............................. 3 2.2 恐慌相關文獻 ............................... 3 第三章 研究架構..................................... 9 3.1 疫情資料蒐集模組............................. 9 3.2 新聞報導資料蒐集模組.......................... 9 3.3 模型建構與分析模組 ........................... 13 第四章 資料介紹..................................... 16 4.1 疫情資料.................................. 16 4.2 新聞媒體資料 ............................... 17 第五章 疫情與恐慌聲量分析............................. 20 5.1 擴散模型演進 ............................... 20 5.2 巴斯擴散模型分析方法.......................... 22 5.3 單向影響擴散模型分析方法 ....................... 23 5.4 基礎再生數分析方法 ........................... 24 5.5 分析結果與討論.............................. 25 第六章 結論....................................... 36 6.1 研究回顧.................................. 36 6.2 研究意義.................................. 37 6.3 研究發展.................................. 37 參考文獻.......................................... 39 | |
| dc.language.iso | zh-TW | |
| dc.subject | 新聞媒體 | zh_TW |
| dc.subject | 新冠肺炎 | zh_TW |
| dc.subject | 疫情 | zh_TW |
| dc.subject | 恐慌 | zh_TW |
| dc.subject | 巴斯模型 | zh_TW |
| dc.subject | 擴散 | zh_TW |
| dc.subject | Bass model | en |
| dc.subject | news | en |
| dc.subject | diffusion | en |
| dc.subject | Coronavirus | en |
| dc.subject | outrage | en |
| dc.subject | COVID-19 | en |
| dc.title | 以擴散模型與基礎再生數分析新冠肺炎疫情與恐慌報導聲量 | zh_TW |
| dc.title | Analyses of the Epidemic of COVID-19 and Keywords of Outrage Components From Mass Media Using Diffusion Models and R0 | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 楊曙榮(Sunny S. Yang) | |
| dc.contributor.oralexamcommittee | 陳聿宏(Yu-Hung Chen) | |
| dc.subject.keyword | 新冠肺炎,疫情,恐慌,巴斯模型,擴散,新聞媒體, | zh_TW |
| dc.subject.keyword | COVID-19,Coronavirus,outrage,Bass model,diffusion,news, | en |
| dc.relation.page | 41 | |
| dc.identifier.doi | 10.6342/NTU202001857 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-07-27 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 商學研究所 | zh_TW |
| 顯示於系所單位: | 商學研究所 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-2607202010081500.pdf 未授權公開取用 | 1.87 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
