Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/146591
|
| Title: | 結合大數據與厚數據方法觀察社群媒介上網紅對閱聽人的情緒渲染效果
Combining Big Data and Thick Data Methods to Analyze the Emotional Contagion Effect of Social Media Influencers on Audience |
| Authors: | 莊喬羽
Chuang, Chiao-Yu |
| Contributors: | 許志堅
Sheu, Jyh-Jian
莊喬羽
Chuang, Chiao-Yu |
| Keywords: | 厚數據
大數據
情緒渲染
網紅
社群媒體
thick data
big data
emotional contagion
internet celebrity
social media |
| Date: | 2023 |
| Issue Date: | 2023-08-02 14:09:21 (UTC+8) |
| Abstract: | 2020年起，受到Covid-19疫情影響，人們進行遠端學習與線上互動的比率大幅提升。台灣民眾使用Youtube平台觀看影片的頻率上升，促進網路紅人（internet celebrity）職業的崛起。網紅進行更加多元化的創作，並快速累積粉絲數，甚至企業與品牌也會邀請網紅拍攝廣告或業配，顯示網紅對市場和民眾的影響力。然而，網紅除了帶動產業發展外，也會影響閱聽人的思想與行為，形成情緒渲染（emotional contagion）現象。
在過去社群媒體的研究中，由於數位足跡（digital footprint）的建立，使大數據研究的資料蒐集更便利。然而，大數據簡化資料中的情感或故事，使研究者難以深入了解使用者脈絡。厚數據（thick data）透過增加資料厚度的方式，除了解決大數據無形中剔除資料中所包含的背景、故事或意義的問題，也能了解人的真實需求。
本研究為分析網紅如何透過影音內容對閱聽人產生情緒渲染效果，以生活娛樂、業配行銷、知識資訊、時事與政治等台灣的四大類型網紅為主，結合大數據和厚數據研究方法增厚數位足跡資料，蒐集總計810部影片樣本資料，來建構影片類目並進行資料分析。經分析結果顯示，網紅可以透過影音內容的情緒表述影響閱聽人產生相似的情緒，且網紅在影片中所使用的新聞時事與政治議題行銷操作手法確實會影響閱聽人的正、負向情緒或態度。其中業配行銷、知識資訊、生活娛樂型網紅較常引起閱聽人的正向情緒，時事與政治型網紅的影片則較常引發閱聽人的負向情緒。不過，從時事與政治型網紅的影片樣本中，我們也發現當影片中的正向和負向情緒比例相近時，負向情緒的感染力更大。此外，在疫情內容主題取樣的影片中，我們觀察到網紅確實會受到重大事件的影響製作影片，並且在相同的影片題材中，網紅在影片中的情緒與態度會改變閱聽人的情緒或態度。
Due to Covid-19, Taiwanese have significantly increased the rate of remote learning and online interaction since 2020. Taiwanese users use Youtube more frequently to watch video contents, which has promoted the rise of the career of internet celebrities. Internet celebrities carry out more diversified creations and quickly accumulate fans. Even enterprises and brands will invite internet celebrities to film the advertisement or create advertorials, showing the influence of internet celebrities on the market and the public. However, the rise of internet celebrities not only drives the development of the industry, but also affects the thoughts and behaviors of audience, resulting in Emotional Contagion.
The establishment of digital footprints make the data collection of big data research more convenient than social media research in the past. But big data simplifies the emotions or stories in the data, making it difficult for researchers to gain a deeper understanding of user context. Thick data increases the thickness of data, which can solve the problem of big data data invisibly eliminating the background, story or meaning contained in the data, and understand the real side of human life.
In order to analyze how internet celebrities have emotional contagion effects on audience through video contents. This study focuses on four types of internet celebrities in Taiwan, including life entertainment, advertorial and marketing, knowledge and information, news and politics. Through big data combined with the thick data manual collection method, we collected a total of 810 videos as samples and constructed video categories.
The results of the analysis show that internet celebrities can influence the audience to have similar emotions through the emotional expression of videos. Moreover, the news and political issues of marketing techniques used by internet celebrities in the videos will indeed affect the positive and negative emotions or attitudes of the audience. And life entertainment, advertorial and marketing, knowledge and information internet celebrities are more likely to arouse positive emotions from the audience, while news and politics internet celebrities are more likely to arouse negative emotions from the audience.However, we also found that the contagious force of negative emotions is greater when the proportion of positive and negative emotions in the film is similar from the video samples of news and politics internet celebrities.
In addition, in the sampled videos of the content of the COVID-19 epidemic, we observed that internet celebrities are indeed affected by major events to make videos, and in the same video theme, the emotions and attitudes of internet celebrities in the videos will change the emotions and attitudes of the audience. |
| Reference: | 一、中文文獻
InsightXplorer 創市際市場研究顧問（2022）。2020 至 2021 年台灣網路行為趨勢觀察與比較。2022年2月15日，取自：https://www.ixresearch.com/reports/創市際雙週刊第一九三期-20220215/
王韋堯、黃詩珮、劉怡寧（2012）。消費品廣告設計之情緒效價與喚起分析。設計學報 (Journal of Design)，17(3)。
王嘉慶（2021）。2021 YouTube Brandcast：能見度及心佔率成品牌溝通關鍵，2022年3月10日。取自：https://taiwan.googleblog.com/2021/10/youtube-brandcast-2021.html
王馥蓓（2018年01月10日）。Youtuber、網紅當道，品牌究竟何去何從？。2022年06月10日，取自：https://www.cw.com.tw/article/5087508
王瀟、李文忠、杜建剛（2010）。情緒感染理論研究述評。心理科學進展。18(8)，1236-1245。
KOL Rader、數位時代（2021）。百大影響力網紅數據洞察報告書。2022年1月20日，取自：https://www.kolradar.com/reports/2021-top100-kol
宋世祥（2016）。百工裡的人類學家：帶你挖掘「厚數據」，以人類學之眼洞悉人性，引領社會創新！。台灣：果力文化出版。
何振誠、邱張名琪、陳威助（2009）。從網誌到微網誌: 網路社會參與型態的演變介紹。資訊社會研究，17，1-51。
林郁翔、任立中（2019）。品牌粉絲專業之社群情感氛圍初探。管理與系統，26(1)，79-112。
林庭安（2020）消費需求藏在數據裡！以人類學視角洞察數據的 3 種工具，幫你看穿消費者情感、思維。經理人，2022年3月25日，取自：https://www.managertoday.com.tw/articles/view/60370
李胤綺（2020）。網紅為政治背書之效果研究。開南學報，27-36。
周得媛、康學真、呂佳妍、謝泓晉（2019）網路紅人可信度影響消費者態度之研究-以 YouTube 表演類網紅為例，圖文傳播藝術學報，87-101。
財團法人台灣網路資訊中心（2020）。2020台灣網路報告，取自：https://report.twnic.tw/2020/
凌品葳（2018）。YouTuber類型與廣告價值對消費者反應之影響：以幽默程度作為調節變數。﹝碩士論文。國立中央大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/8p6uqw。
施伯燁（2014）。社群媒體-使用者研究之概念，方法與方法論初探。傳播研究與實踐，4(2)，207-227。
姜凱恩、洪珮珊、李欣蔚、張家馨（2020）。探討年輕族群觀看 YOUTUBE 影片類型偏好和心理動機。圖文傳播藝術學報，137-144。
袁國寶、謝利明（2016）。網紅經濟:移動互聯網時代的千億紅利市場，台北：商周出版。
陳思涵（2018）。網紅特質對業配效果影響之研究—以美妝品為例。國立中興大學行銷學系所碩士論文，台中市。
陳婉綾、郭宗賢、田寒光（2021）網紅與粉絲關係品質，信任轉移與自我監控對品牌推薦影響之研究。行銷科學學報，17(1)，67-88。
黃友柔（2018），台灣美妝 YouTuber 影片呈現方式對消費者記憶度、喜好度、購買意願之影響，輔仁大學大眾傳播學研究所碩士論文。
黃從仁（2020）。大數據與人工智慧方法在行為與社會科學的應用趨勢。調查研究-方法與應用，(45)，11-42。
曹家榮、陳昭宏（2022）。組裝行動與混成的情緒：Instagram使用者的憂鬱書寫、連結與共生。新聞學研究，(150)，97-148。
程倚華（2021）。網紅人流、金流關鍵指標！「高互動」3大心法：話題、分眾、多平台。2022年4月9日。
程倚華（2021）。獨家調查！2021台灣100大影響力網紅榜單，誰連續3年稱王？誰空降？。2022年1月20日，取自：https://www.bnext.com.tw/article/64752/2021-kol100
楊運秀、郭芳伃（2017）網紅業配文的說服效果: 懷疑人格, 熟悉度及專業性的影響。行銷評論，14(2)，163-189。
蔡政宏、邱惠芳、陳佳玲（2020）。熱門網紅影片內容製作模式之探索性研究。全球商業經營管理學報，(12)，77-88。
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。
劉雨涵（2018）。你 follow 她了嗎? Instagram 網紅的人類學觀察。中央研究院民族學研究所資料彙編，(26)，1-34。
劉致賢（2019）。初探厚資料與中國大陸政治經濟現象: 以國有企業部門為例。問題與研究，58(2)，1-28。
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。中華傳播學會。
劉忠博、陳娟、邵成圓（2019）。中國愛國網紅的民族主義之形成與內涵: 以周小平文章的內容分析為例。傳播文化與政治，(9)，31-61。
鄒妙妍（2018）。網紅。文化研究@嶺南，63(1)，13。
二、英文文獻
Abidin, C. (2018). Internet celebrity: Understanding fame online. Emerald Group Publishing.
Barsade, S. G. (2002). The ripple effect: Emotional contagion and its influence on group behavior. Administrative science quarterly, 47(4), 644-675.
Bulagang, A. F., Weng, N. G., Mountstephens, J., & Teo, J. (2020). A review of recent approaches for emotion classification using electrocardiography and electrodermography signals. Informatics in Medicine Unlocked, 20, 100363.
De Veirman, M., Cauberghe, V., & Hudders, L. (2017). Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. International journal of advertising, 36(5), 798-828.
De Vries, D. A., Möller, A. M., Wieringa, M. S., Eigenraam, A. W., & Hamelink, K. (2018). Social comparison as the thief of joy: Emotional consequences of viewing strangers’ Instagram posts. Media psychology, 21(2), 222-245.
Doherty, R. W. (1997). The emotional contagion scale: A measure of individual differences. Journal of nonverbal Behavior, 21(2), 131-154.
Dols, A. (2019). Emotional Contagion Within Social Media.
Ekman, P., & Friesen, W. V. (1978). Facial action coding system. Environmental Psychology & Nonverbal Behavior.
Fan, R., Xu, K., & Zhao, J. (2018). An agent-based model for emotion contagion and competition in online social media. Physica a: statistical mechanics and its applications, 495, 245-259.
Ferrara, E., & Yang, Z. (2015). Measuring emotional contagion in social media. PloS one, 10(11), e0142390.
Harris, R. B., & Paradice, D. (2007). An investigation of the computer-mediated communication of emotions. Journal of Applied Sciences Research, 3(12), 2081-2090.
Hatfield, E., Cacioppo, J. T., & Rapson, R. L. (1994). Emotional contagion. Cambridge University Press; Editions de la Maison des Sciences de l`Homme.
Hou, M. (2019). Social media celebrity and the institutionalization of YouTube. Convergence, 25(3), 534-553.
Instagram Ranked Worst for Young People’s Mental Health(2017). Retrieved November 22, 2021, from https://www.rsph.org.uk/about-us/news/instagram-ranked-worst-for-young-people-s-mental-health.html
James, W. (2013). What is an Emotion?. Simon and Schuster.
Jin, S. V., Muqaddam, A., & Ryu, E. (2019). Instafamous and social media influencer marketing. Marketing Intelligence & Planning, 37(5), 567-579.
Kemp, S. (2020, February 18). DIGITAL 2020:TAIWAN. https://datareportal.com/reports/digital-2020-taiwan
Kemp, S. (2021, February 11). DIGITAL 2021:TAIWAN. https://datareportal.com/reports/digital-2021-taiwan
Kemp, S. (2022, February 15). DIGITAL 2023:TAIWAN. https://datareportal.com/reports/digital-2021-taiwan
Khamis, S., Ang, L. and Welling, R. (2016), “Self-branding, ‘micro-celebrity’ and the rise of social media influencers”, Celebrity Studies, 8(2), 191-208.
Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business horizons, 54(3), 241-251.
Kleinginna, P. R., & Kleinginna, A. M. (1981). A categorized list of emotion definitions, with suggestions for a consensual definition. Motivation and emotion, 5(4), 345-379.
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.
McKinsey Global Institute. (2011, May 1). Big data: The next frontier for innovation, competition, and productivity. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation
Mayfield, A. (2008). What is social media?
McCracken, G. (1989). Who is the celebrity endorser? Cultural foundations of the endorsement process. Journal of consumer research, 16(3), 310-321.
Mehrabian, A., & Russell, J. A. (1974). The basic emotional impact of environments. Perceptual and motor skills, 38(1), 283-301.
Ménard, M., Richard, P., Hamdi, H., Daucé, B., & Yamaguchi, T. (2015). Emotion Recognition based on Heart Rate and Skin Conductance. In PhyCS, 26-32.
NapoleonCat(2021, November 22). Instagram users in Taiwan. https://napoleoncat.com/stats/instagram-users-in-taiwan/2021/01/
Papacharissi, Z. (2015). Affective publics: Sentiment, technology, and politics. Oxford University Press.
Poster, M. (2006). Information please: Culture and politics in the age of digital machines. Duke University Press.
Russell, J. A. (1979). Affective space is bipolar. Journal of personality and social psychology, 37(3), 345.
Russell, J. A. (1980). A circumplex model of affect. Journal of personality and social psychology, 39(6), 1161.
Scherer, K. R. (2005). What are emotions? And how can they be measured?. Social science information, 44(4), 695-729.
Schoenewolf, G. (1990). Emotional contagion: Behavioral induction in individuals and groups. Modern Psychoanalysis, 15(1), 49-61.
Socialbakers(2021, January 20). Report: State of Social Media and CX - Q2 2021. https://go.emplifi.io/social-media-trends-q2-2021.html
Smets, A., & Lievens, B. (2018, October). Human Sensemaking in the Smart City: A Research Approach Merging Big and Thick Data. In Ethnographic Praxis in Industry Conference Proceedings (Vol. 2018, No. 1, pp. 179-194).
Tomkins, S. S. (1984). Affect theory. Approaches to emotion, 163(163-195), 31-65.
Tricia Wang. (2016, Jan 21). Why Big Data Needs Thick Data?. https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7
Turkle, S. (2012). Alone together: Why we expect more from technology and less from each other. New York, NY: Basic Books.
Van Kleef, G. A. (2010). The emerging view of emotion as social information. Social and Personality Psychology Compass, 4(5), 331–343.
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: the PANAS scales. Journal of personality and social psychology, 54(6), 1063.
Werner Geyser. (2022, February 7). The State of Influencer Marketing 2022: Benchmark Report. Influencer Marketing Hub. https://influencermarketinghub.com/influencer-marketing-benchmark-report/
ZAC(2020, February). The New Rules of Social. https://www.zakagency.com/wp-content/uploads/2020/02/J2097_WP3_TheNewRulesOfSocial_Compressed.pdf |
| Description: | 碩士
國立政治大學
傳播學院傳播碩士學位學程
109464016 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109464016 |
| Data Type: | thesis |
| Appears in Collections: | [傳播學院傳播碩士學位學程] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 401601.pdf | | 3313Kb | Adobe PDF2 | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
