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題名 社群新聞閱聽人情緒分析：以新冠肺炎疫情為例
Emotional Analysis of Social Media News Readers: The Case of Covid-19 Pandemic作者 盧羿彣
Lu, Yi-Wen貢獻者 許志堅
盧羿彣
Lu, Yi-Wen關鍵詞 新冠肺炎
社群媒體新聞
情緒渲染效應
厚數據分析
COVID-19
social media news
emotional contagion
thick data analysis日期 2024 上傳時間 1-Feb-2024 11:29:19 (UTC+8) 摘要 本研究旨在探討新冠肺炎疫情期間新聞報導面向與使用者情緒的整體變化為何，是否存在情緒渲染效果，並進一步了解不同政治立場媒體之報導面向與使用者情緒的異同。本研究使用大數據與厚數據的混合方法，研究區間為2020年1月1日至2023年5月30日，研究對象為台灣四家媒體《TVBS新聞》、《udn.com聯合新聞網》、《三立新聞》、《自由時報》的Facebook粉絲專業，共分析1,661則新聞與435,561則留言。 研究結果顯示，媒體傾向以非正面的角度報導疫情新聞事件。六個報導面向中，「對民眾的鼓舞」和「描述疫情災難與損害」面向比例最高，其他面向相對較少。使用者情緒部分，幾乎所有新聞事件中的主要情緒皆為「憤怒」，僅有「指揮中心解編」事件以「肯定」情緒為主。「憤怒」情緒的對象共有五類，最常見的是對政府的憤怒。 大多數新聞事件均驗證了情緒渲染效應的假設，即正面報導引發較高正面情緒，而非正面報導引發較高負面情緒。然而，兩起新聞事件中出現了相反趨勢，「AZ疫苗不良反應」新聞中，正面報導引發較高「憤怒」情緒，非正面報導則引發較高「肯定」與「祈福」情緒；而「口罩解封」新聞中，正面報導反而引發較高「恐慌」情緒。 不同政治立場媒體下的報導面向和使用者情緒略有不同。偏綠媒體使用「肯定防疫成效」、「對民眾的鼓舞」面向的比率普遍高於偏藍媒體，偏藍媒體使用「責任歸屬與批判」面向的比率通常高於偏綠媒體。另外，使用者情緒受到媒體政治立場的影響較大。偏藍媒體新聞引發的「憤怒」情緒普遍高於偏綠媒體；而偏綠媒體引發的「肯定」和「祈福」情緒普遍高於偏藍媒體；在「恐慌」與「悲傷」情緒方面，不同媒體之間的差異則不太明顯。
This study aims to investigate the news framing and emotional responses on social media during the COVID-19 pandemic. It explores the presence of emotional contagion effects and examines the differences in news framing and emotional responses among media outlets with different political stances. Employing a mixed-method approach involving big data and thick data, the research spans from January 1, 2020, to May 30, 2023, focusing on the Facebook fan pages of four Taiwanese media outlets, TVBS, United Daily News, SET News and Liberty Times. A total of 1,661 news articles and 435,561 comments were analyzed. The findings indicate that the media tended to frame COVID-19 news events from a negative perspective. Among the six identified news frames, the highest proportions were found in "encouraging the public" and "describing the disasters and damages of COVID-19". Regarding user emotions, "anger" emerged as the predominant emotion across almost all news events, with diverse targets, and was most commonly directed towards the government. While the majority of news events supported the hypothesis of emotional contagion effects, where positive reporting triggered higher positive emotions and vice versa, two specific news events exhibited contrary trends. In the "AZ vaccine side effect" news event, positive reporting led to higher "anger" emotions, whereas negative reporting resulted in higher "approval" and "blessing" emotions. In the "Mask restriction loosen" news event, positive reporting surprisingly elicited higher "panic" emotions. Differences were observed in news framing and user emotions across media outlets with different political stances. Pro-DPP media outlets tended to utilize "affirmation of policy effectiveness" and "encouraging the public" frame more frequently than pro-KMT media outlets, while the latter tended to employ the "responsibility and criticism" frame more often. Furthermore, user emotions were significantly influenced by the political leanings of the media outlets. News from pro-KMT media outlets generally triggered higher "anger" emotions compared to pro-DPP media outlets, while pro-DPP media outlets evoked higher "approval" and "blessing" emotions.參考文獻 林雨佑、楊惠君、林慧貞、陳潔、楊智強、嚴文廷、曹馥年、張詩芸、洪琴宣、陳思樺、許佳琦（2022年8月10日）。COVID-19大事記：從全球到台灣，疫情如何發展？報導者。https://www.twreporter.org/a/2019-ncov-epidemic 李宜芳（2021年6月4日）。讀懂疫情》有情有義的「日台友誼」之外，日本捐贈的124萬劑疫苗怎麼來的？公視新聞網。https://news.pts.org.tw/article/529353 財團法人台灣網路資訊中心（2022年7月20日）。《台灣網路報告》：22-24。https://report.twnic.tw/2022/assets/download/TWNIC_TaiwanInternetReport_2022_CH.pdf 張茗喧（2020年12月20日）。紐西蘭籍機師確診前曾在北部活動，指揮中心急查足跡。中央社。https://www.cna.com.tw/news/firstnews/202012205006.aspx 張茗喧、吳欣紜（2021年3月4日）。指揮中心：首批AZ疫苗須謹慎 不拚7天內開打。中央社。https://www.cna.com.tw/news/firstnews/202103040100.aspx 賴淑敏、蔣龍祥、黃韻玲（2021年3月12日）。新增6境外移入病例 多國停打AZ疫苗指揮中心暫不跟進。公視新聞網。https://news.pts.org.tw/article/516948 衛生福利部（2020年4月30日）。防疫樂活新態度，指揮中心啟動「防疫新生活運動」，請多多響應。衛生福利部。https://www.mohw.gov.tw/cp-4633-52991-1.html 衛生福利部（2020年9月3日）。查獲加利科技有限公司以非醫用口罩混充實名制醫用口罩銷售之說明。衛生福利部。https://www.mohw.gov.tw/cp-4627-55529-1.html 衛生福利部（2021年5月7日）。指揮中心說明諾富特飯店員工確診COVID-19事件裁罰。衛生福利部。https://www.mohw.gov.tw/cp-5016-59563-1.html 衛生福利部（2022年3月）。守護全民健康福祉 共創經濟防疫新模式。衛生福利部季刊。https://www.mohwpaper.tw/adv3/maz32/utx01.asp 衛生福利部（2023年4月25日）。拍板同意指揮中心解編及防疫降階 陳揆感謝全民團結合作，並責成衛福部成立「COVID-19防治聯繫會報」。衛生福利部。https://www.mohw.gov.tw/cp-6565-74426-1.html 衛生福利部（2020年4月27日）。護臺灣．助世界。口罩響應人道援助，幫助世界全民一起。衛生福利部。https://www.mohw.gov.tw/cp-4633-52932-1.html 衛生福利部疾病管制署（2020年2月18日）。日本鑽石公主號列武漢肺炎感染區，我國旅客將包機返台隔離檢疫。衛生福利部疾病管制署。https://www.cdc.gov.tw/Category/ListContent/EmXemht4IT-IRAPrAnyG9A?uaid=72_znES_r55fdi25zEttnw 衛生福利部疾病管制署（2021年7月23日）。指揮中心自7月27日至8月9日調降疫情警戒標準至第二級，請民眾持續配合防疫措施，守護彼此健康。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/e0v8zImE3rGJ0072A86NHA?typeid=9 衛生福利部疾病管制署（2022年5月25日）。指揮中心修訂病例定義，民眾使用家用抗原快篩試劑檢測結果陽性，經醫事人員確認即為確診。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/Z-Wb81S3V9f4hHHJoMoZgA?typeid=9 衛生福利部疾病管制署（2020年2月4日）。武漢返台台商已安置完畢，嚴格隔離檢疫14天。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/i8FtkMxozYsmyQMWEuOvug?typeid=9 衛生福利部疾病管制署（2020年11月18日）。12月1日秋冬防疫專案啟動，請民眾及醫療院所主動配合相關措施。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/56UPsWnK5KgAKolUMz7uWw?typeid=9 衛生福利部疾病管制署（2021年9月2日）。感謝台積電、鴻海永齡、慈濟三間企業和民間團體共同捐贈1,500萬劑BNT疫苗，第一批93萬劑疫苗於9月2日上午7時抵臺。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/Hs6PgiLrDThHBXH02Vgv1A?typeid=9 衛生福利部疾病管制署（2022年11月28日）。12月1日起適度放寬戴口罩等防疫措施。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/6HMlB14lmBfMTwKhc8h6mw?typeid=9 劉昌德（2020）。〈小編新聞學：社群媒體與通訊軟體如何轉化新聞專業〉，《新聞學研究》，142：1-58。 劉慧雯、柯籙晏（2016）。〈邁向厚數據：以「詮釋型資訊工具」進行意義分析的概念基礎〉，發表於「中華傳播學會2016年年會」。嘉義：中正大學。 Aggarwal, C. C. (2011). Data Mining in Social Media (pp. 327-352). Springer. https://doi.org/10.1007/978-1-4419-8462-3_12 Al-Rawi, A. (2019). Viral News on Social Media [Article]. Digital Journalism, 7(1), 63-79. https://doi.org/10.1080/21670811.2017.1387062 Albris, K., Otto, E. I., Astrupgaard, S. L., Gregersen, E. M., Jorgensen, L. S., Jorgensen, O., Sandbye, C. R., & Schonning, S. (2021). A view from anthropology: Should anthropologists fear the data machines? [Article]. Big Data & Society, 8(2), 7, Article 20539517211043655. https://doi.org/10.1177/20539517211043655 Ananiadou, S. (2008). National centre for text mining: Introduction to tools for researchers. Retrieved from http://www.jisc.ac.uk/publications/publications/bpnationalcentrefortextminingv1.aspx Anderson, M., & Caumont, A. (2014, September 24). How social media is reshaping news. Retrieved from Pew Research Center Website http://www.pewresearch.org/fact-tank/2014/09/24/how-social-media-is-reshaping-news/ Anzur, T. (2000). How to Talk To the Media: Televised Coverage of Public Health Issues in a Disaster. Prehospital and Disaster Medicine, 15(4), 70-72. doi:10.1017/S1049023X00025310 Aslam, M-Faheem & Awan, Tahir & Syed, Jabir Hussain & Kashif, Aisha & Parveen, Mahwish. (2020). Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanities and Social Sciences Communications. 7. 10.1057/s41599-020-0523-3. Balzarotti, S., & Ciceri, M. R. (2014). News Reports of Catastrophes and Viewers' Fear: Threat Appraisal of Positively Versus Negatively Framed Events. Media Psychology, 17(4), 357-377. https://doi.org/10.1080/15213269.2013.826588 Barsade, S. G. (2002). The Ripple Effect: Emotional Contagion and Its Influence on Group Behavior. Administrative science quarterly, 47(4), 644-675. https://doi.org/10.2307/3094912 Blanchard-Coehm, R. D. (1998). Understanding Public Response to Increased Risk from Natural Hazards: Application of the Hazards Risk Communication Framework. International Journal of Mass Emergencies & Disasters, 16(3), 247–278. https://doi.org/10.1177/028072709801600302 Boczkowski, P. J., Mitchelstein, E., & Matassi, M. (2018). "News comes across when I'm in a moment of leisure": Understanding the practices of incidental news consumption on social media [Article]. New Media & Society, 20(10), 3523-3539. https://doi.org/10.1177/1461444817750396 Boelle, J., & Wahl-Jorgensen, K. (2022). Emotionality in the Television Coverage of Airplane Disasters [Article; Early Access]. Journalism Practice, 17. https://doi.org/10.1080/17512786.2022.2085618 Bornakke, T., & Due, B. L. (2018). Big-Thick Blending: A method for mixing analytical insights from big and thick data sources [Article]. Big Data & Society, 5(1), 16, Article 2053951718765026. https://doi.org/10.1177/2053951718765026 Casero-Ripolles, A. (2020). Impact of Covid-19 on the media system. Communicative and democratic consequences of news consumption during the outbreak [Article]. Profesional De La Informacion, 29(2), 11, Article e290223. https://doi.org/10.3145/epi.2020.mar.23 Centers for Disease Control and Prevention. (2020). Pandemic Influenza. Retrieved from https://www.cdc.gov/flu/pandemic-resources/index.htm Cook, F. L., Tyler, T. R., Goetz, E. G., Gordon, M. T., Protess, D., Leff, D. R., & Molotch, H. L. (1983). Media and agenda setting: Effects on the public, interest group leaders, policy makers, and policy. Public Opinion Quarterly, 47(1), 16-35. https://doi.org/https://doi.org/10.1086/268764 Dörre, J., Gerstl, P., & Seiffert, R. (1999). Text mining: finding nuggets in mountains of textual data Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, San Diego, California, USA. https://doi.org/10.1145/312129.312299 Ferrara, E., & Yang, Z. Y. (2015). Measuring Emotional Contagion in Social Media [Article]. Plos One, 10(11), 14, Article e0142390. https://doi.org/10.1371/journal.pone.0142390 Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter Bubbles, Echo Chambers, and Online News Consumption [Article]. Public Opinion Quarterly, 80, 298-320. https://doi.org/10.1093/poq/nfw006 Goldenberg, A., & Gross, J. J. (2020). Digital Emotion Contagion. Trends in Cognitive Sciences, 24(4), 316-328. https://doi.org/https://doi.org/10.1016/j.tics.2020.01.009 Goode, L. (2009). Social news, citizen journalism and democracy. New Media & Society, 11(8), 1287-1305. https://doi.org/10.1177/1461444809341393 Guadagno, R. E., Rempala, D. M., Murphy, S., & Okdie, B. M. (2013). What makes a video go viral? An analysis of emotional contagion and Internet memes [Article]. Computers in Human Behavior, 29(6), 2312-2319. https://doi.org/10.1016/j.chb.2013.04.016 Hancock, J., Gee, K., Ciaccio, K., & Lin, J. (2008). I'm sad you're sad: Emotional contagion in CMC. https://doi.org/10.1145/1460563.1460611 Hatfield, E., Cacioppo, J. T., & Rapson, R. L. (1993). Emotional Contagion. Current Directions in Psychological Science, 2(3), 96-100. https://doi.org/10.1111/1467-8721.ep10770953 He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2013.01.001 Hermida, A., Fletcher, F., Korell, D., & Logan, D. (2012). Share, Like, Recommend Decoding the Social Media News Consumer [Article]. Journalism Studies, 13(5-6), 815-824. https://doi.org/10.1080/1461670x.2012.664430 Karlsen, R., & Aalberg, T. (2021). Social Media and Trust in News: An Experimental Study of the Effect of Facebook on News Story Credibility [Article; Early Access]. Digital Journalism, 17. https://doi.org/10.1080/21670811.2021.1945938 Kelly, H. (2011). The classical definition of a pandemic is not elusive. Bulletin of the World Health Organization, 89(7), 540-541. https://doi.org/10.2471/BLT.11.089086 Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks [Article]. Proceedings of the National Academy of Sciences of the United States of America, 111(24), 8788-8790. https://doi.org/10.1073/pnas.1320040111 Kumpel, A. S. (2022). Social Media Information Environments and Their Implications for the Uses and Effects of News: The PINGS Framework [Article]. Communication Theory, 32(2), 223-242. https://doi.org/10.1093/ct/qtab012 Latzko-Toth, G., Bonneau, C., & Millette, M. (2017). Small data, thick data: Thickening strategies for trace-based social media research. The SAGE handbook of social media research methods, 199-214. Lee, C. S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience [Article]. Computers in Human Behavior, 28(2), 331-339. https://doi.org/10.1016/j.chb.2011.10.002 Lerman, K. (2007). Social Information Processing in News Aggregation. IEEE Internet Computing, 11(6), 16-28. https://doi.org/10.1109/MIC.2007.136 Littlefield, R. S., & Quenette, A. M. (2007). Crisis leadership and Hurricane Katrina: The portrayal of authority by the media in natural disasters [Article]. Journal of Applied Communication Research, 35(1), 26-47. https://doi.org/10.1080/00909880601065664 Liu, B. F., Fraustino, J. D., & Jin, Y. (2016). Social Media Use During Disasters: How Information Form and Source Influence Intended Behavioral Responses [Article]. Communication Research, 43(5), 626-646. https://doi.org/10.1177/0093650214565917 Macias, W., Hilyard, K., & Freimuth, V. (2009). Blog Functions as Risk and Crisis Communication During Hurricane Katrina. J. Computer-Mediated Communication, 15, 1-31. https://doi.org/10.1111/j.1083-6101.2009.01490.x McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard business review, 90, 60-66, 68, 128. National Science and Technology Council. (2005). Grand challenges for disaster reduction: A report of the subcommittee on disaster reduction. Washington, DC: National Science and Technology Council, Executive Office of the President. Retrieved from http://www.sdr.gov/docs/SDRGrandChallengesforDisasterReduction.pdf Newman, N. (2009). The rise of social media and its impact on mainstream journalism: A study of how newspapers and broadcasters in the UK and US are responding to a wave of participatory social media, and a historic shift in control towards individual consumers. Working Paper. Reuters Institute for the Study of Journalism, University of Oxford Newman, N., Fletcher R., Kalogeropoulos A., Levy D., & Nielsen R. (2018). Reuters Institute Digital News Report 2018. Reuters Institute for the Study of Journalism. Retrieved from https://reutersinstitute.politics.ox.ac.uk/sites/default/files/digital-news-report-2018.pdf Ni, M. Y., Yang, L., Leung, C. M. C., Li, N., Yao, X. I., Wang, Y. S., Leung, G. M., Cowling, B. J., & Liao, Q. Y. (2020). Mental Health, Risk Factors, and Social Media Use During the COVID-19 Epidemic and Cordon Sanitaire Among the Community and Health Professionals in Wuhan, China: Cross-Sectional Survey [Article]. Jmir Mental Health, 7(5), 6, Article e19009. https://doi.org/10.2196/19009 Pantti, M. (2010). The value of emotion: An examination of television journalists’ notions on emotionality. European Journal of Communication - EUR J COMMUN, 25, 168-181. https://doi.org/10.1177/0267323110363653 Pantti, M., & Wahl-Jorgensen, K. (2007). On the political possibilities of therapy news: Social responsibility and the limits of objectivity in disaster coverage. Tijdschrift Voor Communicatiewetenschappen, 1. Park, C. S., & Kaye, B. K. (2018). News Engagement on Social Media and Democratic Citizenship: Direct and Moderating Roles of Curatorial News Use in Political Involvement [Article]. Journalism & Mass Communication Quarterly, 95(4), 1103-1127. https://doi.org/10.1177/1077699017753149 Peters, C. (2011). Emotion aside or emotional side? Crafting an ‘experience of involvement’ in the news. Journalism, 12, 297-316. https://doi.org/10.1177/1464884910388224 Reynolds, B., & Seeger, M. W. (2005). Crisis and emergency risk communication as an integrative model [Article; Proceedings Paper]. Journal of Health Communication, 10(1), 43-55. https://doi.org/10.1080/10810730590904571 Rimé, B. (2009). Emotion Elicits the Social Sharing of Emotion: Theory and Empirical Review. Emotion Review, 1, 60-85. https://doi.org/10.1177/1754073908097189 Schneider, J. (2018). EMOTIONAL CONTAGION THROUGH ONLINE NEWSPAPERS. Paper presented at the European Conference on Information Systems (p.171). Schultz, F., Utz, S., & Göritz, A. (2011). Is the Medium the Message? Perceptions of and Reactions to Crisis Communication via Twitter, Blogs and Traditional Media. Public Relations Review, 37, 20-27. https://doi.org/10.1016/j.pubrev.2010.12.001 Shearer, E., & Gottfried, J. (2017, September 7). News Use Across Social Media Platforms in 2017. Retrieved from Pew Research Center Website https://www.pewresearch.org/journalism/2017/09/07/news-use-across-social-media-platforms-2017/ Shearer, E., & Mitchell A. (2021, January 12). News Use Across Social Media Platforms in 2020. Retrieved from Pew Research Center Website https://www.pewresearch.org/journalism/2021/01/12/news-use-across-social-media-platforms-in-2020/ Singer, J. B. (2011). Participatory journalism : guarding open gates at online newspapers / Jane B. Singer ... [et al.]. Wiley-Blackwell. Small, D. A., & Verrochi, N. M. (2009). The Face of Need: Facial Emotion Expression on Charity Advertisements [Article]. Journal of Marketing Research, 46(6), 777-787. https://doi.org/10.1509/jmkr.46.6.777 Smith, L. W., & Rose, R. L. (2020). Service with a smiley face: Emojional contagion in digitally mediated relationships. International Journal of Research in Marketing, 37(2), 301-319. https://doi.org/https://doi.org/10.1016/j.ijresmar.2019.09.004 Storey, M.-A. (2016). Lies, damned lies, and analytics: Why big data needs thick data. In Perspectives on Data Science for Software Engineering (pp. 369-374). Elsevier. Su, C. N. (2020). Feeling the catastrophe: the interplay between emotional story-telling and journalistic authority in the televisual construction of natural disasters [Article]. Asian Journal of Communication, 30(5), 363-385. https://doi.org/10.1080/01292986.2020.1813182 Tandoc, E. C., & Vos, T. P. (2016). The Journalist is Marketing the News Social media in the gatekeeping process [Article]. Journalism Practice, 10(8), 950-966. https://doi.org/10.1080/17512786.2015.1087811 Tang, J. Y., Yu, G., & Yao, X. X. (2021). Emotional Contagion in the Online Depression Community [Article]. Healthcare, 9(12), 14, Article 1609. https://doi.org/10.3390/healthcare9121609 Tierney, K., Bevc, C., & Kuligowski, E. (2006). Metaphors matter: Disaster myths, media frames, and their consequences in Hurricane Katrina [Article]. Annals of the American Academy of Political and Social Science, 604, 57-81. https://doi.org/10.1177/0002716205285589 Vasterman, P., Yzermans, C. J., & Dirkzwager, A. J. E. (2005). The role of the media and media hypes in the aftermath of disasters [Article]. Epidemiologic Reviews, 27, 107-114. https://doi.org/10.1093/epirev/mxi002 Wang, T. (2016, January 20). Why Big Data Needs Thick Data. Ethnography Matters. Medium channel. Retrieved from https://medium.com/ethnography-matters/why-big-data-needs-thick-datab4b3e75e3d7#.y9plmare1 Wenger, D. E., Dykes, J. D., Sebok, T. D., & Neff, J. L. (1975). It’s a matter of myths: An empirical examination of individual insight into disaster response. Mass Emergencies, 1, 3346. Wenger, Dennis & E. L. Quarantelli. (1989). Local Mass Media Operations, Problems, and Products in Disasters. Newark, DE: University of Delaware, Disaster Research Center. Yell, S. (2010). 'Breakfast is now tea, toast and tissues': Affect and the Media Coverage of Bushfires [Article]. Media International Australia(137), 109-119. <Go to ISI>://WOS:000285401300012 Yell, S. (2012). Natural disaster news and communities of feeling: the affective interpellation of local and global publics [Article]. Social Semiotics, 22(4), 409-428. https://doi.org/10.1080/10350330.2012.693295 Zhang, X. Z., & Li, W. S. (2020). From Social Media with News: Journalists' Social Media Use for Sourcing and Verification [Article]. Journalism Practice, 14 (10), 1193-1210. https://doi.org/10.1080/17512786.2019.1689372 描述 碩士
國立政治大學
傳播學院傳播碩士學位學程
110464037資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110464037 資料類型 thesis dc.contributor.advisor 許志堅 zh_TW dc.contributor.author (Authors) 盧羿彣 zh_TW dc.contributor.author (Authors) Lu, Yi-Wen en_US dc.creator (作者) 盧羿彣 zh_TW dc.creator (作者) Lu, Yi-Wen en_US dc.date (日期) 2024 en_US dc.date.accessioned 1-Feb-2024 11:29:19 (UTC+8) - dc.date.available 1-Feb-2024 11:29:19 (UTC+8) - dc.date.issued (上傳時間) 1-Feb-2024 11:29:19 (UTC+8) - dc.identifier (Other Identifiers) G0110464037 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/149608 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 傳播學院傳播碩士學位學程 zh_TW dc.description (描述) 110464037 zh_TW dc.description.abstract (摘要) 本研究旨在探討新冠肺炎疫情期間新聞報導面向與使用者情緒的整體變化為何，是否存在情緒渲染效果，並進一步了解不同政治立場媒體之報導面向與使用者情緒的異同。本研究使用大數據與厚數據的混合方法，研究區間為2020年1月1日至2023年5月30日，研究對象為台灣四家媒體《TVBS新聞》、《udn.com聯合新聞網》、《三立新聞》、《自由時報》的Facebook粉絲專業，共分析1,661則新聞與435,561則留言。 研究結果顯示，媒體傾向以非正面的角度報導疫情新聞事件。六個報導面向中，「對民眾的鼓舞」和「描述疫情災難與損害」面向比例最高，其他面向相對較少。使用者情緒部分，幾乎所有新聞事件中的主要情緒皆為「憤怒」，僅有「指揮中心解編」事件以「肯定」情緒為主。「憤怒」情緒的對象共有五類，最常見的是對政府的憤怒。 大多數新聞事件均驗證了情緒渲染效應的假設，即正面報導引發較高正面情緒，而非正面報導引發較高負面情緒。然而，兩起新聞事件中出現了相反趨勢，「AZ疫苗不良反應」新聞中，正面報導引發較高「憤怒」情緒，非正面報導則引發較高「肯定」與「祈福」情緒；而「口罩解封」新聞中，正面報導反而引發較高「恐慌」情緒。 不同政治立場媒體下的報導面向和使用者情緒略有不同。偏綠媒體使用「肯定防疫成效」、「對民眾的鼓舞」面向的比率普遍高於偏藍媒體，偏藍媒體使用「責任歸屬與批判」面向的比率通常高於偏綠媒體。另外，使用者情緒受到媒體政治立場的影響較大。偏藍媒體新聞引發的「憤怒」情緒普遍高於偏綠媒體；而偏綠媒體引發的「肯定」和「祈福」情緒普遍高於偏藍媒體；在「恐慌」與「悲傷」情緒方面，不同媒體之間的差異則不太明顯。 zh_TW dc.description.abstract (摘要) This study aims to investigate the news framing and emotional responses on social media during the COVID-19 pandemic. It explores the presence of emotional contagion effects and examines the differences in news framing and emotional responses among media outlets with different political stances. Employing a mixed-method approach involving big data and thick data, the research spans from January 1, 2020, to May 30, 2023, focusing on the Facebook fan pages of four Taiwanese media outlets, TVBS, United Daily News, SET News and Liberty Times. A total of 1,661 news articles and 435,561 comments were analyzed. The findings indicate that the media tended to frame COVID-19 news events from a negative perspective. Among the six identified news frames, the highest proportions were found in "encouraging the public" and "describing the disasters and damages of COVID-19". Regarding user emotions, "anger" emerged as the predominant emotion across almost all news events, with diverse targets, and was most commonly directed towards the government. While the majority of news events supported the hypothesis of emotional contagion effects, where positive reporting triggered higher positive emotions and vice versa, two specific news events exhibited contrary trends. In the "AZ vaccine side effect" news event, positive reporting led to higher "anger" emotions, whereas negative reporting resulted in higher "approval" and "blessing" emotions. In the "Mask restriction loosen" news event, positive reporting surprisingly elicited higher "panic" emotions. Differences were observed in news framing and user emotions across media outlets with different political stances. Pro-DPP media outlets tended to utilize "affirmation of policy effectiveness" and "encouraging the public" frame more frequently than pro-KMT media outlets, while the latter tended to employ the "responsibility and criticism" frame more often. Furthermore, user emotions were significantly influenced by the political leanings of the media outlets. News from pro-KMT media outlets generally triggered higher "anger" emotions compared to pro-DPP media outlets, while pro-DPP media outlets evoked higher "approval" and "blessing" emotions. en_US dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與問題 2 第二章 文獻回顧 5 第一節 新冠肺炎疫情 5 第二節 災難傳播 10 第三節 情緒渲染 13 第四節 社群媒介 16 第五節 文字探勘與厚數據 19 第三章 研究方法 22 第一節 研究方法與架構 22 第二節 樣本選取 23 第三節 類目建構 26 第四章 資料分析 29 第一節 新聞基本資料 29 第二節 各疫情階段新聞報導面向與使用者情緒分析 32 第三節 不同政治立場媒體報導面向與使用者情緒差異 155 第五章 結論與建議 170 第一節 研究發現概述 170 第二節 研究結果討論 172 第三節 研究限制與未來建議 181 參考文獻 185 zh_TW dc.format.extent 2562920 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110464037 en_US dc.subject (關鍵詞) 新冠肺炎 zh_TW dc.subject (關鍵詞) 社群媒體新聞 zh_TW dc.subject (關鍵詞) 情緒渲染效應 zh_TW dc.subject (關鍵詞) 厚數據分析 zh_TW dc.subject (關鍵詞) COVID-19 en_US dc.subject (關鍵詞) social media news en_US dc.subject (關鍵詞) emotional contagion en_US dc.subject (關鍵詞) thick data analysis en_US dc.title (題名) 社群新聞閱聽人情緒分析：以新冠肺炎疫情為例 zh_TW dc.title (題名) Emotional Analysis of Social Media News Readers: The Case of Covid-19 Pandemic en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 林雨佑、楊惠君、林慧貞、陳潔、楊智強、嚴文廷、曹馥年、張詩芸、洪琴宣、陳思樺、許佳琦（2022年8月10日）。COVID-19大事記：從全球到台灣，疫情如何發展？報導者。https://www.twreporter.org/a/2019-ncov-epidemic 李宜芳（2021年6月4日）。讀懂疫情》有情有義的「日台友誼」之外，日本捐贈的124萬劑疫苗怎麼來的？公視新聞網。https://news.pts.org.tw/article/529353 財團法人台灣網路資訊中心（2022年7月20日）。《台灣網路報告》：22-24。https://report.twnic.tw/2022/assets/download/TWNIC_TaiwanInternetReport_2022_CH.pdf 張茗喧（2020年12月20日）。紐西蘭籍機師確診前曾在北部活動，指揮中心急查足跡。中央社。https://www.cna.com.tw/news/firstnews/202012205006.aspx 張茗喧、吳欣紜（2021年3月4日）。指揮中心：首批AZ疫苗須謹慎 不拚7天內開打。中央社。https://www.cna.com.tw/news/firstnews/202103040100.aspx 賴淑敏、蔣龍祥、黃韻玲（2021年3月12日）。新增6境外移入病例 多國停打AZ疫苗指揮中心暫不跟進。公視新聞網。https://news.pts.org.tw/article/516948 衛生福利部（2020年4月30日）。防疫樂活新態度，指揮中心啟動「防疫新生活運動」，請多多響應。衛生福利部。https://www.mohw.gov.tw/cp-4633-52991-1.html 衛生福利部（2020年9月3日）。查獲加利科技有限公司以非醫用口罩混充實名制醫用口罩銷售之說明。衛生福利部。https://www.mohw.gov.tw/cp-4627-55529-1.html 衛生福利部（2021年5月7日）。指揮中心說明諾富特飯店員工確診COVID-19事件裁罰。衛生福利部。https://www.mohw.gov.tw/cp-5016-59563-1.html 衛生福利部（2022年3月）。守護全民健康福祉 共創經濟防疫新模式。衛生福利部季刊。https://www.mohwpaper.tw/adv3/maz32/utx01.asp 衛生福利部（2023年4月25日）。拍板同意指揮中心解編及防疫降階 陳揆感謝全民團結合作，並責成衛福部成立「COVID-19防治聯繫會報」。衛生福利部。https://www.mohw.gov.tw/cp-6565-74426-1.html 衛生福利部（2020年4月27日）。護臺灣．助世界。口罩響應人道援助，幫助世界全民一起。衛生福利部。https://www.mohw.gov.tw/cp-4633-52932-1.html 衛生福利部疾病管制署（2020年2月18日）。日本鑽石公主號列武漢肺炎感染區，我國旅客將包機返台隔離檢疫。衛生福利部疾病管制署。https://www.cdc.gov.tw/Category/ListContent/EmXemht4IT-IRAPrAnyG9A?uaid=72_znES_r55fdi25zEttnw 衛生福利部疾病管制署（2021年7月23日）。指揮中心自7月27日至8月9日調降疫情警戒標準至第二級，請民眾持續配合防疫措施，守護彼此健康。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/e0v8zImE3rGJ0072A86NHA?typeid=9 衛生福利部疾病管制署（2022年5月25日）。指揮中心修訂病例定義，民眾使用家用抗原快篩試劑檢測結果陽性，經醫事人員確認即為確診。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/Z-Wb81S3V9f4hHHJoMoZgA?typeid=9 衛生福利部疾病管制署（2020年2月4日）。武漢返台台商已安置完畢，嚴格隔離檢疫14天。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/i8FtkMxozYsmyQMWEuOvug?typeid=9 衛生福利部疾病管制署（2020年11月18日）。12月1日秋冬防疫專案啟動，請民眾及醫療院所主動配合相關措施。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/56UPsWnK5KgAKolUMz7uWw?typeid=9 衛生福利部疾病管制署（2021年9月2日）。感謝台積電、鴻海永齡、慈濟三間企業和民間團體共同捐贈1,500萬劑BNT疫苗，第一批93萬劑疫苗於9月2日上午7時抵臺。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/Hs6PgiLrDThHBXH02Vgv1A?typeid=9 衛生福利部疾病管制署（2022年11月28日）。12月1日起適度放寬戴口罩等防疫措施。衛生福利部疾病管制署。https://www.cdc.gov.tw/Bulletin/Detail/6HMlB14lmBfMTwKhc8h6mw?typeid=9 劉昌德（2020）。〈小編新聞學：社群媒體與通訊軟體如何轉化新聞專業〉，《新聞學研究》，142：1-58。 劉慧雯、柯籙晏（2016）。〈邁向厚數據：以「詮釋型資訊工具」進行意義分析的概念基礎〉，發表於「中華傳播學會2016年年會」。嘉義：中正大學。 Aggarwal, C. C. (2011). Data Mining in Social Media (pp. 327-352). Springer. https://doi.org/10.1007/978-1-4419-8462-3_12 Al-Rawi, A. (2019). Viral News on Social Media [Article]. Digital Journalism, 7(1), 63-79. https://doi.org/10.1080/21670811.2017.1387062 Albris, K., Otto, E. I., Astrupgaard, S. L., Gregersen, E. M., Jorgensen, L. S., Jorgensen, O., Sandbye, C. R., & Schonning, S. (2021). A view from anthropology: Should anthropologists fear the data machines? [Article]. Big Data & Society, 8(2), 7, Article 20539517211043655. https://doi.org/10.1177/20539517211043655 Ananiadou, S. (2008). National centre for text mining: Introduction to tools for researchers. Retrieved from http://www.jisc.ac.uk/publications/publications/bpnationalcentrefortextminingv1.aspx Anderson, M., & Caumont, A. (2014, September 24). How social media is reshaping news. Retrieved from Pew Research Center Website http://www.pewresearch.org/fact-tank/2014/09/24/how-social-media-is-reshaping-news/ Anzur, T. (2000). How to Talk To the Media: Televised Coverage of Public Health Issues in a Disaster. Prehospital and Disaster Medicine, 15(4), 70-72. doi:10.1017/S1049023X00025310 Aslam, M-Faheem & Awan, Tahir & Syed, Jabir Hussain & Kashif, Aisha & Parveen, Mahwish. (2020). Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak. Humanities and Social Sciences Communications. 7. 10.1057/s41599-020-0523-3. Balzarotti, S., & Ciceri, M. R. (2014). News Reports of Catastrophes and Viewers' Fear: Threat Appraisal of Positively Versus Negatively Framed Events. Media Psychology, 17(4), 357-377. https://doi.org/10.1080/15213269.2013.826588 Barsade, S. G. (2002). The Ripple Effect: Emotional Contagion and Its Influence on Group Behavior. Administrative science quarterly, 47(4), 644-675. https://doi.org/10.2307/3094912 Blanchard-Coehm, R. D. (1998). Understanding Public Response to Increased Risk from Natural Hazards: Application of the Hazards Risk Communication Framework. International Journal of Mass Emergencies & Disasters, 16(3), 247–278. https://doi.org/10.1177/028072709801600302 Boczkowski, P. J., Mitchelstein, E., & Matassi, M. (2018). "News comes across when I'm in a moment of leisure": Understanding the practices of incidental news consumption on social media [Article]. New Media & Society, 20(10), 3523-3539. https://doi.org/10.1177/1461444817750396 Boelle, J., & Wahl-Jorgensen, K. (2022). Emotionality in the Television Coverage of Airplane Disasters [Article; Early Access]. Journalism Practice, 17. https://doi.org/10.1080/17512786.2022.2085618 Bornakke, T., & Due, B. L. (2018). Big-Thick Blending: A method for mixing analytical insights from big and thick data sources [Article]. Big Data & Society, 5(1), 16, Article 2053951718765026. https://doi.org/10.1177/2053951718765026 Casero-Ripolles, A. (2020). Impact of Covid-19 on the media system. Communicative and democratic consequences of news consumption during the outbreak [Article]. Profesional De La Informacion, 29(2), 11, Article e290223. https://doi.org/10.3145/epi.2020.mar.23 Centers for Disease Control and Prevention. (2020). Pandemic Influenza. Retrieved from https://www.cdc.gov/flu/pandemic-resources/index.htm Cook, F. L., Tyler, T. R., Goetz, E. G., Gordon, M. T., Protess, D., Leff, D. R., & Molotch, H. L. (1983). Media and agenda setting: Effects on the public, interest group leaders, policy makers, and policy. Public Opinion Quarterly, 47(1), 16-35. https://doi.org/https://doi.org/10.1086/268764 Dörre, J., Gerstl, P., & Seiffert, R. (1999). Text mining: finding nuggets in mountains of textual data Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, San Diego, California, USA. https://doi.org/10.1145/312129.312299 Ferrara, E., & Yang, Z. Y. (2015). Measuring Emotional Contagion in Social Media [Article]. Plos One, 10(11), 14, Article e0142390. https://doi.org/10.1371/journal.pone.0142390 Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter Bubbles, Echo Chambers, and Online News Consumption [Article]. Public Opinion Quarterly, 80, 298-320. https://doi.org/10.1093/poq/nfw006 Goldenberg, A., & Gross, J. J. (2020). Digital Emotion Contagion. Trends in Cognitive Sciences, 24(4), 316-328. https://doi.org/https://doi.org/10.1016/j.tics.2020.01.009 Goode, L. (2009). Social news, citizen journalism and democracy. New Media & Society, 11(8), 1287-1305. https://doi.org/10.1177/1461444809341393 Guadagno, R. E., Rempala, D. M., Murphy, S., & Okdie, B. M. (2013). What makes a video go viral? An analysis of emotional contagion and Internet memes [Article]. Computers in Human Behavior, 29(6), 2312-2319. https://doi.org/10.1016/j.chb.2013.04.016 Hancock, J., Gee, K., Ciaccio, K., & Lin, J. (2008). I'm sad you're sad: Emotional contagion in CMC. https://doi.org/10.1145/1460563.1460611 Hatfield, E., Cacioppo, J. T., & Rapson, R. L. (1993). Emotional Contagion. Current Directions in Psychological Science, 2(3), 96-100. https://doi.org/10.1111/1467-8721.ep10770953 He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2013.01.001 Hermida, A., Fletcher, F., Korell, D., & Logan, D. (2012). Share, Like, Recommend Decoding the Social Media News Consumer [Article]. Journalism Studies, 13(5-6), 815-824. https://doi.org/10.1080/1461670x.2012.664430 Karlsen, R., & Aalberg, T. (2021). Social Media and Trust in News: An Experimental Study of the Effect of Facebook on News Story Credibility [Article; Early Access]. Digital Journalism, 17. https://doi.org/10.1080/21670811.2021.1945938 Kelly, H. (2011). The classical definition of a pandemic is not elusive. Bulletin of the World Health Organization, 89(7), 540-541. https://doi.org/10.2471/BLT.11.089086 Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks [Article]. Proceedings of the National Academy of Sciences of the United States of America, 111(24), 8788-8790. https://doi.org/10.1073/pnas.1320040111 Kumpel, A. S. (2022). Social Media Information Environments and Their Implications for the Uses and Effects of News: The PINGS Framework [Article]. Communication Theory, 32(2), 223-242. https://doi.org/10.1093/ct/qtab012 Latzko-Toth, G., Bonneau, C., & Millette, M. (2017). Small data, thick data: Thickening strategies for trace-based social media research. The SAGE handbook of social media research methods, 199-214. Lee, C. S., & Ma, L. (2012). News sharing in social media: The effect of gratifications and prior experience [Article]. Computers in Human Behavior, 28(2), 331-339. https://doi.org/10.1016/j.chb.2011.10.002 Lerman, K. (2007). Social Information Processing in News Aggregation. IEEE Internet Computing, 11(6), 16-28. https://doi.org/10.1109/MIC.2007.136 Littlefield, R. S., & Quenette, A. M. (2007). Crisis leadership and Hurricane Katrina: The portrayal of authority by the media in natural disasters [Article]. Journal of Applied Communication Research, 35(1), 26-47. https://doi.org/10.1080/00909880601065664 Liu, B. F., Fraustino, J. D., & Jin, Y. (2016). Social Media Use During Disasters: How Information Form and Source Influence Intended Behavioral Responses [Article]. Communication Research, 43(5), 626-646. https://doi.org/10.1177/0093650214565917 Macias, W., Hilyard, K., & Freimuth, V. (2009). Blog Functions as Risk and Crisis Communication During Hurricane Katrina. J. Computer-Mediated Communication, 15, 1-31. https://doi.org/10.1111/j.1083-6101.2009.01490.x McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard business review, 90, 60-66, 68, 128. National Science and Technology Council. (2005). Grand challenges for disaster reduction: A report of the subcommittee on disaster reduction. Washington, DC: National Science and Technology Council, Executive Office of the President. Retrieved from http://www.sdr.gov/docs/SDRGrandChallengesforDisasterReduction.pdf Newman, N. (2009). The rise of social media and its impact on mainstream journalism: A study of how newspapers and broadcasters in the UK and US are responding to a wave of participatory social media, and a historic shift in control towards individual consumers. Working Paper. Reuters Institute for the Study of Journalism, University of Oxford Newman, N., Fletcher R., Kalogeropoulos A., Levy D., & Nielsen R. (2018). Reuters Institute Digital News Report 2018. Reuters Institute for the Study of Journalism. Retrieved from https://reutersinstitute.politics.ox.ac.uk/sites/default/files/digital-news-report-2018.pdf Ni, M. Y., Yang, L., Leung, C. M. C., Li, N., Yao, X. I., Wang, Y. S., Leung, G. M., Cowling, B. J., & Liao, Q. Y. (2020). Mental Health, Risk Factors, and Social Media Use During the COVID-19 Epidemic and Cordon Sanitaire Among the Community and Health Professionals in Wuhan, China: Cross-Sectional Survey [Article]. Jmir Mental Health, 7(5), 6, Article e19009. https://doi.org/10.2196/19009 Pantti, M. (2010). The value of emotion: An examination of television journalists’ notions on emotionality. European Journal of Communication - EUR J COMMUN, 25, 168-181. https://doi.org/10.1177/0267323110363653 Pantti, M., & Wahl-Jorgensen, K. (2007). On the political possibilities of therapy news: Social responsibility and the limits of objectivity in disaster coverage. Tijdschrift Voor Communicatiewetenschappen, 1. Park, C. S., & Kaye, B. K. (2018). News Engagement on Social Media and Democratic Citizenship: Direct and Moderating Roles of Curatorial News Use in Political Involvement [Article]. Journalism & Mass Communication Quarterly, 95(4), 1103-1127. https://doi.org/10.1177/1077699017753149 Peters, C. (2011). Emotion aside or emotional side? Crafting an ‘experience of involvement’ in the news. Journalism, 12, 297-316. https://doi.org/10.1177/1464884910388224 Reynolds, B., & Seeger, M. W. (2005). Crisis and emergency risk communication as an integrative model [Article; Proceedings Paper]. Journal of Health Communication, 10(1), 43-55. https://doi.org/10.1080/10810730590904571 Rimé, B. (2009). Emotion Elicits the Social Sharing of Emotion: Theory and Empirical Review. Emotion Review, 1, 60-85. https://doi.org/10.1177/1754073908097189 Schneider, J. (2018). EMOTIONAL CONTAGION THROUGH ONLINE NEWSPAPERS. Paper presented at the European Conference on Information Systems (p.171). Schultz, F., Utz, S., & Göritz, A. (2011). Is the Medium the Message? Perceptions of and Reactions to Crisis Communication via Twitter, Blogs and Traditional Media. Public Relations Review, 37, 20-27. https://doi.org/10.1016/j.pubrev.2010.12.001 Shearer, E., & Gottfried, J. (2017, September 7). News Use Across Social Media Platforms in 2017. Retrieved from Pew Research Center Website https://www.pewresearch.org/journalism/2017/09/07/news-use-across-social-media-platforms-2017/ Shearer, E., & Mitchell A. (2021, January 12). News Use Across Social Media Platforms in 2020. Retrieved from Pew Research Center Website https://www.pewresearch.org/journalism/2021/01/12/news-use-across-social-media-platforms-in-2020/ Singer, J. B. (2011). Participatory journalism : guarding open gates at online newspapers / Jane B. Singer ... [et al.]. Wiley-Blackwell. Small, D. A., & Verrochi, N. M. (2009). The Face of Need: Facial Emotion Expression on Charity Advertisements [Article]. Journal of Marketing Research, 46(6), 777-787. https://doi.org/10.1509/jmkr.46.6.777 Smith, L. W., & Rose, R. L. (2020). Service with a smiley face: Emojional contagion in digitally mediated relationships. International Journal of Research in Marketing, 37(2), 301-319. https://doi.org/https://doi.org/10.1016/j.ijresmar.2019.09.004 Storey, M.-A. (2016). Lies, damned lies, and analytics: Why big data needs thick data. In Perspectives on Data Science for Software Engineering (pp. 369-374). Elsevier. Su, C. N. (2020). Feeling the catastrophe: the interplay between emotional story-telling and journalistic authority in the televisual construction of natural disasters [Article]. Asian Journal of Communication, 30(5), 363-385. https://doi.org/10.1080/01292986.2020.1813182 Tandoc, E. C., & Vos, T. P. (2016). The Journalist is Marketing the News Social media in the gatekeeping process [Article]. Journalism Practice, 10(8), 950-966. https://doi.org/10.1080/17512786.2015.1087811 Tang, J. Y., Yu, G., & Yao, X. X. (2021). Emotional Contagion in the Online Depression Community [Article]. Healthcare, 9(12), 14, Article 1609. https://doi.org/10.3390/healthcare9121609 Tierney, K., Bevc, C., & Kuligowski, E. (2006). Metaphors matter: Disaster myths, media frames, and their consequences in Hurricane Katrina [Article]. Annals of the American Academy of Political and Social Science, 604, 57-81. https://doi.org/10.1177/0002716205285589 Vasterman, P., Yzermans, C. J., & Dirkzwager, A. J. E. (2005). The role of the media and media hypes in the aftermath of disasters [Article]. Epidemiologic Reviews, 27, 107-114. https://doi.org/10.1093/epirev/mxi002 Wang, T. (2016, January 20). Why Big Data Needs Thick Data. Ethnography Matters. Medium channel. Retrieved from https://medium.com/ethnography-matters/why-big-data-needs-thick-datab4b3e75e3d7#.y9plmare1 Wenger, D. E., Dykes, J. D., Sebok, T. D., & Neff, J. L. (1975). It’s a matter of myths: An empirical examination of individual insight into disaster response. Mass Emergencies, 1, 3346. Wenger, Dennis & E. L. Quarantelli. (1989). Local Mass Media Operations, Problems, and Products in Disasters. Newark, DE: University of Delaware, Disaster Research Center. Yell, S. (2010). 'Breakfast is now tea, toast and tissues': Affect and the Media Coverage of Bushfires [Article]. Media International Australia(137), 109-119. <Go to ISI>://WOS:000285401300012 Yell, S. (2012). Natural disaster news and communities of feeling: the affective interpellation of local and global publics [Article]. Social Semiotics, 22(4), 409-428. https://doi.org/10.1080/10350330.2012.693295 Zhang, X. Z., & Li, W. S. (2020). From Social Media with News: Journalists' Social Media Use for Sourcing and Verification [Article]. Journalism Practice, 14 (10), 1193-1210. https://doi.org/10.1080/17512786.2019.1689372 zh_TW
