Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/147118| 題名: | 社群災難新聞主題對應用戶情緒與眾議之厚數據分析 Thick Data Analysis of Social Disaster News Topics Corresponding to User Emotions and Public Opinions |
作者: | 王蔓菁 Wang, Man-Ching |
貢獻者: | 許志堅 王蔓菁 Wang, Man-Ching |
關鍵詞: | 災難新聞 社群新聞主題 社群用戶反應行為 厚數據分析 |
日期: | 2023 | 上傳時間: | 1-Sep-2023 | 摘要: | 本研究旨在分析社群災難新聞中，不同的新聞主題導致的社群用戶留言情 緒與討論議題之差異，進一步以天災與人禍事件新聞對比，了解不同的災難類 型產生的不同用戶反應現象。本研究使用厚數據研究中的人工收集數位足跡編碼方法，研究樣本事件為 2021 至 2022 期間發生的天災與人禍事件，天災事件 為 2022 年尼莎颱風淹水、2022 年台東大地震、2022 高雄雷雨淹水水災，人禍 事件為 2021 年太魯閣列車出軌事故、2021 年高雄城中城火災、2022 年新竹輪 胎行火災。本研究獲取新聞樣本之對象為影響力排行前五的 Facebook 新聞粉絲 專頁《ETtoday 新聞雲》、《東森新聞》、《udn.com 聯合新聞網》、《TVBS 新聞》、《三立新聞》，共收集 641 則新聞樣本數據。\n研究結果顯示，641 則新聞中，責任歸屬的新聞主題更多;用戶情緒類目 中，天災的總統計比例最多之情緒為厭惡情緒，人禍總統計比例最多之情緒為 憤怒情緒;用戶討論議題類目中，天災的總統計比例最多之討論議題為其他議 題，人禍的總統計比例最多之討論議題為制度探討議題。研究發現，單一留言 可能含有零到多種情緒和討論議題;天災新聞沒有準確的咎責對象，留言區立 場混亂，人禍新聞則有咎責對象，用戶立場一致性更高，情緒集中度較天災新 聞更高。總體而言，本研究深入觀察與分析社群災難新聞中，用戶非理性留言 中的情緒，以及用戶理性留言的討論議題，留下專門對於此世代的台灣社群傳 播現象紀錄。 | 參考文獻: | 〈2019 年台灣媒體使用行為調查〉(2019)。取自台灣民意教育基金會 https://www.taiwan-panorama.com/Articles/Details?Guid=4ee4d26b-4e67-4444- befa-25f96098812c&CatId=9\n林又青、何瑞益、王俞婷、傅鏸漩、梁庭語、施虹如 李威霖、陳珮琦、林聖 琪、呂喬茵、朱崇銳、李士強 劉哲欣、張志新(2021 年 12 月)〈111 年 度豪雨及颱風事件災情彙整報告〉，《全球災害事件簿》取自 https://den.ncdr.nat.gov.tw/media/19041/111%E5%B9%B4%E5%BA%A6%E8 %B1%AA%E9%9B%A8%E5%8F%8A%E9%A2%B1%E9%A2%A8%E4%BA %8B%E4%BB%B6%E7%81%BD%E6%83%85%E5%BD%99%E6%95%B4% E5%A0%B1%E5%91%8A.pdf\n溫慧敏. (2016). 迎向生命的曙光—淺談八仙塵爆事件. 護理雜誌, 63(1), 17-21. 0402 臺鐵第 408 次車清水隧道重大鐵道事故. (2021 年 5 月). 0402 臺鐵第 408 次\n車清水隧道重大鐵道事故. 國家運輸安全調查委員會. https://www.ttsb.gov.tw/media/5326/0402%E8%87%BA%E9%90%B5%E7%A C%AC408%E6%AC%A1%E8%BB%8A%E6%B8%85%E6%B0%B4%E9%9A %A7%E9%81%93%E9%87%8D%E5%A4%A7%E9%90%B5%E9%81%93%E 4%BA%8B%E6%95%85%E8%AA%BF%E6%9F%A5%E5%A0%B1%E5%91 %8A.pdf\n《災難防救法》(2022 年 6 月 15 日)。全國法規資料庫，取自 https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=D0120014\n國家地震工程研究中心(2022)〈2022.09.17 台東地震 初始災損報告(第二 版)〉《國家實驗研究院》取自 https://www.ncree.narl.org.tw/assets/file/20220917_%E5%8F%B0%E6%9D%B\n119\n1%E5%9C%B0%E9%9C%87%E5%BD%99%E6%95%B4%E7%B0%A1%E5\n%A0%B1_V2.3.pdf\n〈2022 台灣網路報告〉(無日期)。取自財團法人台灣網路資訊中心網頁 https://report.twnic.tw/2022/assets/download/TWNIC_TaiwanInternetReport_20 22_CH.pdf\n行銷人(2018 年 4 月 15 日)。〈FB 社群龍地位不保?台灣網路社群趨勢全分 析〉。取自 https://www.marketersgo.com/marketing/201804/2017-social- media-analysis-report/\n1014 城中城火災行政調查小組(2021 年 10 月 29 日)〈高雄市鹽埕區城中城火 災事件之行政調查報告〉，《高雄市政府》 https://ws.kcg.gov.tw/001/KcgUploadFiles/263/relfile/9460/71041/4ad8fe1c- 05f5-4a75-9c0e-4cfef9e4f3e6.pdf\n朱家儀. (2022 年 6 月 16 日). 新竹市輪胎汽修廠疑家庭爭吵憤而縱火，釀全家 8 死 1 傷悲劇，消防局記者會說明案情. 關鍵評論網. https://www.thenewslens.com/article/168317\n國家災害防救科技中心. (2020 年 4 月). 2019 天然災害紀實. 國家災害防救科技 中心. https://www.itdr.tw/dispPageBox/getFile/GetView.aspx?FileLocation=PJ- SITEVC%5CFiles%5CPrjFiles%5C140%5C&FileFullName=%E5%85%A8%E 6%96%87%E5%A0%B1%E5%91%8A.pdf&FileName=FR3098\n高雄市仁武區公所. (2020 年 4 月). 高雄市仁武區 111 年水災危險潛勢地區保全 計畫. 高雄市仁武區公所. https://orgws.kcg.gov.tw/001/KcgOrgUploadFiles/210/relfile/0/82172/571e8960 -084b-4f2c-809a-64ad7cb7d542.pdf\n蔡孟妤、曾以寧. (2020 年 8 月 28 日). 高雄大雨 路樹倒塌壓毀汽車、燕巢 200\n多戶停電. CNN 中央通訊社.\nhttps://www.cna.com.tw/news/ahel/202208280194.aspx\n120\n\n黃惠萍. (2003). 媒介框架之預設判準效應與閱聽人的政策評估-以核四案為 例. 新聞學研究, (77), 67-105.\n郭毓倫. (2021). 大數據視角下的公共政策-網路輿情分析方法之應用與發展. 中 國地方自治, 74(9), 3-35.\n劉正山. (2019). 厚資料與意義探勘專刊導論. 問題與研究, 58(2), i-vi.\n王石番. (1991). 傳播內容分析法: 理論與實證. 幼獅文化事業公司.\n蘇蘅. (2019). 傳播研究方法新論. 台北: 雙葉書廊.\nEckstein, D., Künzel, V., & Schäfer, L. (2021). Global climate risk index 2021. Who\nSuffers Most from Extreme Weather Events, 2000-2019. DATAREPORTAL(2022) . DIGITAL 2022: TAIWAN. Retrieved from\nDATAREPORTAL Web site:\nhttps://kepios.com/?utm_campaign=Cross_Promotion&utm_content=Footer_Co\npyright_Notice&utm_medium=Hyperlink&utm_source=DataReportal\nLevy, D., Newman, N., Fletcher, R., Kalogeropoulos, A., & Nielsen, R. K. (2017). Reuters institute digital news report 2014. Report of the Reuters Institute for the Study of Journalism.\nCeron, A., & Negri, F. (2016). The “social side” of public policy: Monitoring online public opinion and its mobilization during the policy cycle. Policy &\nInternet, 8(2), 131-147.\nCoombs, W. T. (2021). Ongoing crisis communication: Planning, managing, and responding. Sage publications.\nSarwar, D. (2019). An Overview of Disaster and Emergency Management Systems Models. Emergency and Disaster Management: Concepts, Methodologies, Tools, and Applications, 32-43.\n121\n\nTorani, S., Majd, P. M., Maroufi, S. S., Dowlati, M., & Sheikhi, R. A. (2019). The importance of education on disasters and emergencies: A review article. Journal of education and health promotion, 8.\nFritz, C. (1957). II: Disasters Compared in Six American Communities. Human Organization, 16(2), 6-9.\nZhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018). A survey on deep learning for big data. Information Fusion, 42, 146-157.\nTumasjan, A., Sprenger, T., Sandner, P., & Welpe, I. (2010, May). Predicting elections with twitter: What 140 characters reveal about political sentiment. In Proceedings of the international AAAI conference on web and social media (Vol. 4, No. 1, pp. 178-185).\nBollen, J., Mao, H., & Pepe, A. (2011). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the international AAAI conference on web and social media (Vol. 5, No. 1, pp. 450-453).\nHermida, A., Fletcher, F., Korell, D., & Logan, D. (2012). Share, like, recommend: Decoding the social media news consumer. Journalism studies, 13(5-6), 815- 824.\nTandoc Jr, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing?. Computers in human behavior, 43, 139-146.\nBaumeister, R. F., & Leary, M. R. (1995). The need to belong: desire for interpersonal attachments as a fundamental human motivation. Psychological bulletin, 117(3), 497.\nKitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big data & society, 1(1), 2053951714528481.\n122\n\nTsou, M. H. (2015). Research challenges and opportunities in mapping social media and Big Data. Cartography and Geographic Information Science, 42(sup1), 70- 74.\nLaney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.\nElish, M. C., & Boyd, D. (2018). Situating methods in the magic of Big Data and AI. Communication monographs, 85(1), 57-80.\nAltay, S., Hacquin, A. S., & Mercier, H. (2022). Why do so few people share fake news? It hurts their reputation. new media & society, 24(6), 1303-1324.\nTierney, K. J., Lindell, M. K., & Perry, R. W. (2002). Facing the unexpected: disaster preparedness and response in the United States. Disaster Prevention and Management: An International Journal, 11(3), 222-222.\nSellnow, T. L., & Seeger, M. W. (2021). Theorizing crisis communication. John Wiley & Sons.\nKramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788-8790.\nGoffman, E. (1974). Frame analysis: An essay on the organization of experience. Harvard University Press.\nGamson, W. A., & Modigliani, A. (1987). The changing culture of affirmative action. Research in political sociology, 3(1), 137-177.\nEntman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of communication, 43(4), 51-58.\nMiles, B., & Morse, S. (2007). The role of news media in natural disaster risk and recovery. Ecological economics, 63(2-3), 365-373.\n123\n\nStephenson, R., & Anderson, P. S. (1997). Disasters and the information technology revolution. Disasters, 21(4), 305-334.\nVasterman, P., Yzermans, C. J., & Dirkzwager, A. J. (2005). The role of the media and media hypes in the aftermath of disasters. Epidemiologic reviews, 27(1), 107-114.\nFisher Liu, B. (2009). An analysis of US government and media disaster frames. Journal of Communication Management, 13(3), 268-283.\nSemetko, H. A., & Valkenburg, P. M. (2000). Framing European politics: A content analysis of press and television news. Journal of communication, 50(2), 93-109.\nKuttschreuter, M., Gutteling, J. M., & De Hond, M. (2011). Framing and tone-of- voice of disaster media coverage: The aftermath of the Enschede fireworks disaster in the Netherlands. Health, risk & society, 13(3), 201-220.\nGortner, E. M., & Pennebaker, J. W. (2003). The archival anatomy of a disaster: Media coverage and community-wide health effects of the Texas A&M bonfire tragedy. Journal of Social and Clinical Psychology, 22(5), 580-603.\nMoritz, M., & Crapanzano, T. (2010). We don’t make the news, we just report it: Television journalism and narratives of trauma. Miscelánea: A Journal of English and American Studies, 42.\nGreenberg, J., & Scanlon, T. J. (2016). Old media, new media, and the complex story of disasters. In Oxford Research Encyclopedia of Natural Hazard Science.\nPoudel, B. R., FitzGerald, G., Clark, M. J., Mehta, A., & Poudyal Chhetri, M. B. (2014, August). How does Nepal Television (NTV) frame natural disasters? A qualitative content analysis of news scripts using news frames and PPRR cycle. In 5th International Disaster and Risk Conference (IDRC 2014) (pp. 583-586).\nRimé, B. (2009). Emotion elicits the social sharing of emotion: Theory and empirical\nreview. Emotion review, 1(1), 60-85.\n124\n\nYell, S. (2012). Natural disaster news and communities of feeling: The affective interpellation of local and global publics. Social Semiotics, 22(4), 409-428.\nGranatt, M. (1999). Civil emergencies and the media: A central government perspective. In Shirley Harrison (Ed.), Disasters and the media: Managing crisis communications (pp. 101-117). London, UK: Macmillan Business.\nSinger, E., & Endreny, P. M. (1993). Reporting on risk: How the mass media portray accidents, diseases, disaster, and other hazards. New York, NY: Russell Sage Foundation.\nChoi, Y., & Lin, Y. H. (2008). A content analysis of the newspaper coverage of the three major hurricanes in 2005. Public Relations Review, 34(3), 294-296.\nMarcus, G. E., MacKuen, M., & Neuman, W. R. (2011). Parsimony and complexity: Developing and testing theories of affective intelligence. Political Psychology, 32(2), 323-336.\nEkman, P. (1992). An argument for basic emotions. Cognition & emotion, 6(3-4), 169-200.\nBatbaatar, E., Li, M., & Ryu, K. H. (2019). Semantic-emotion neural network for emotion recognition from text. IEEE access, 7, 111866-111878.\nTomkins, S. (1962). Affect imagery consciousness: Volume I: The positive affects. Springer publishing company.\nSainger, G. (2021). Sentiment Analysis-An Assessment of Online Public Opinion: A Conceptual Review. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(5), 1881-1887.\nDe los Santos, T. M., & Nabi, R. L. (2019). Emotionally charged: Exploring the role of emotion in online news information seeking and processing. Journal of Broadcasting & Electronic Media, 63(1), 39-58.\n125\n\nYang, Y., Xiu, L., & Yu, G. (2021). Emotional Information in News Reporting on Audience Cognitive Processing in the Age of Posttruth: An Electroencephalogram and Functional Connectivity Approach. Frontiers in Psychology, 12, 734147.\nKemavuthanon, K., & Uchida, O. (2020). Classification of social media messages posted at the time of disaster. In Information Technology in Disaster Risk Reduction: 4th IFIP TC 5 DCITDRR International Conference, ITDRR 2019, Kyiv, Ukraine, October 9–10, 2019, Revised Selected Papers 4 (pp. 212-226). Springer International Publishing.\nGu, M., Guo, H., Zhuang, J., Du, Y., & Qian, L. (2022). Social media user behavior and emotions during crisis events. International journal of environmental research and public health, 19(9), 5197.\nHyvärinen, H., & Beck, R. (2018). Emotions trump facts: The role of emotions in on social media: A literature review.\nGruebner, O., Rapp, M. A., Adli, M., Kluge, U., Galea, S., & Heinz, A. (2017). Cities and mental health. Deutsches Ärzteblatt International, 114(8), 121.\nZhang, Y., Fang, Y., Quan, X., Dai, L., Si, L., & Yuan, X. (2012, August). Emotion tagging for comments of online news by meta classification with heterogeneous information sources. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (pp. 1059- 1060).\nHille, S., & Bakker, P. (2014). Engaging the social news user: Comments on news sites and Facebook. Journalism Practice, 8(5), 563-572.\nLi, Q., Wang, J., Chen, Y. P., & Lin, Z. (2010). User comments for news recommendation in forum-based social media. Information Sciences, 180(24), 4929-4939.\n126\n\nHouston, J. B., Hansen, G. J., & Nisbett, G. S. (2011). Influence of user comments on perceptions of media bias and third-person effect in online news. Electronic News, 5(2), 79-92.\nLiu, S. B., Palen, L., Sutton, J., Hughes, A. L., & Vieweg, S. (2008, May). In search of the bigger picture: The emergent role of on-line photo sharing in times of disaster. In Proceedings of the information systems for crisis response and management conference (ISCRAM) (pp. 4-7). Citeseer.\nTankard, J. W. (1991). Media frames: Approaches to conceptualization and measurement.\nYang, T., Xie, J., Li, G., Mou, N., Li, Z., Tian, C., & Zhao, J. (2019). Social media big data mining and spatio-temporal analysis on public emotions for disaster mitigation. ISPRS International Journal of Geo-Information, 8(1), 29.\nDiebold, F. X. (2003, February). Big data dynamic factor models for macroeconomic measurement and forecasting. In Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress of the Econometric Society,”(edited by M. Dewatripont, LP Hansen and S. Turnovsky) (Vol. 115, p. 22).\nAnuradha, J. (2015). A brief introduction on Big Data 5Vs characteristics and Hadoop technology. Procedia computer science, 48, 319-324.\nFan, W., & Bifet, A. (2013). Mining big data: current status, and forecast to the future. ACM SIGKDD explorations newsletter, 14(2), 1-5.\nManovich, L. (2011). Trending: The promises and the challenges of big social data. Debates in the digital humanities, 2(1), 460-475.\nBruns, A., & Burgess, J. (2011). The use of Twitter hashtags in the formation of ad hoc publics. In Proceedings of the 6th European consortium for political\n127\n\nresearch (ECPR) general conference 2011 (pp. 1-9). The European Consortium\nfor Political Research (ECPR).\nBornakke, T., & Due, B. L. (2018). Big–Thick Blending: A method for mixing\nanalytical insights from big and thick data sources. Big Data & Society, 5(1),\n2053951718765026.\nBrewer, J., & Hunter, A. (1989). Multimethod research: A synthesis of styles. Sage\nPublications, Inc.\nDiakopoulos, N., & Naaman, M. (2011, March). Towards quality discourse in online\nnews comments. In Proceedings of the ACM 2011 conference on Computer\nsupported cooperative work (pp. 133-142).\nMcCluskey, M., & Hmielowski, J. (2012). Opinion expression during social conflict:\nComparing online reader comments and letters to the editor. Journalism, 13(3),\n303-319.\nMadden, A., Ruthven, I., & McMenemy, D. (2013). A classification scheme for\ncontent analyses of YouTube video comments. Journal of documentation, 69(5),\n693-714.\nLatzko-Toth, G., Bonneau, C., & Millette, M. (2017). Small data, thick data:\nThickening strategies for trace-based social media research. The SAGE handbook\nof social media research methods, 199-214.\nBabbie, E. R. (2020). The practice of social research. Cengage learning. Holsti, O. R. (1969). Content analysis for the social sciences and\nhumanities. Reading. MA: Addison-Wesley (content analysis).\nWimmer, R. D., & Dominick, J. R. (2013). Mass media research. Cengage learning. | 描述: | 碩士 國立政治大學 傳播學院傳播碩士學位學程 110464050 |
資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0110464050 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 405001.pdf | 5.29 MB | Adobe PDF2 | View/Open |
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.