學術產出-Theses

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

  • No doi shows Citation Infomation
題名 意見一致性、潛水動機與潛水行為初探:社群聆聽技術與調查法之比較分析
Exploring Relations among Opinion Congruency, Lurking Motives and Behavior: Social Listening versus Survey Method
作者 王嘉呈
貢獻者 張郁敏
王嘉呈
關鍵詞 潛水者
潛水動機
意見一致性
沈默螺旋
社群聆聽
情緒分析
Lurker
Lurker motives
Opinion congruency
Spiral of silence
Social listening
Sentiment analysis
日期 2017
上傳時間 1-Nov-2017 14:33:20 (UTC+8)
摘要 社群網站使用者不分年齡,幾乎沒有人不在這虛擬社交的浪潮上。儘管如此, 社群網站的交往卻不如現實般,社群中的絕大多數的內容是由少數發言者貢獻, 從來不發言的潛水者則佔了使用者基數的大部分。
本研究使用沈默螺旋理論的意見一致性概念與多種潛水動機作連接,藉此探 討發言者言論如何影響潛水者的動機選擇以及潛水行為表現。除此外,本研究藉 由同時使用社群聆聽技術和調查法作為研究方法,試圖以主、客觀區分兩種方法 並比較各自的益處和限制,也對社群聆聽技術只能使用發言者言論作為分析資料 來源的先天限制做出初步探討。
本研究收集到 599 份有效問卷和 285 篇社群網站文章,研究結果發現害怕被 孤立、社會性散漫兩種潛水動機完全中介了意見一致性對潛水行為的效果。主、 客觀研究方法的測量結果顯著相關,且對潛水動機之中介效果有相同預測能力。
It is hard to find one had no experience using social networks in any age ranges. However, most of social network members are lurkers who barely post or comment to express their opinion. On the other hand, little regular posters contribute most content in every virtual society.
This study used the concept of opinion congruency in spiral of silence theory to link up multiple lurking motives found by past studies in order to clarify how posters’ texts influence lurking motives and behavior. Besides, for the purpose of comparing pros and cons between social listening and survey, this study adopted both research methods to measure major opinion in discussion threads wherea seprated the two methods into subjective and objective ones. Also, this study would have preliminary discuss about the fact of limited analytical source of social listening.
Collected 599 valid surveys and 285 social network discuss thread text, the result found that opinion congruency negatively influenced both lurking motives which positively influenced lurking behavior. The result also found that the subjective and objective research methods in this study were significantly related, and shared same predictive ability on both lurking motives’ mediated effect.
參考文獻 一、中文部分
〈歷年個人及家庭上網行為趨勢分析〉(2016年7月)。取自台灣網路資訊中心http://www.twnic.net.tw/download/200307/20160922f.pdf
〈《網路社群調查》逾92%網友在討論區找購物資訊〉(2015年11月11日)。取自資策會產業情報研究所https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=411
〈八成以上台灣人愛用Facebook、Line坐穩社群網站龍頭 1人平均擁4個社群帳號,年輕人更愛YouTube和IG〉(2017年5月1日)。取自資策會http://www.iii.org.tw/Press/NewsDtl.aspx?nsp_sqno=1934&fm_sqno=14
i-Buzz口碑研究中心(2017年3月22日)。〈論壇始祖PTT反映社會價值觀,後起之秀Dcard成年輕族群新據點〉,《動腦雜誌》。取自http://www.brain.com.tw/news/articlecontent?ID=44582&sort=#FOZw8s6e
李明穎 (2012)。〈網路潛水者的公民參與實踐之探索:以 「野草莓運動」為例〉,《新聞學研究》,112: 77-116。
林奇秀、陳一帆(2011)。〈淺析網路社群知識分享實證研究如何構思社會資本概念〉,《圖書資訊學刊》,9(2): 55-89。
林秀雲譯(2013)。《社會科學研究方法》,台北:聖智學習。(原書 Babbie, E. [2013]. The Practice of Social Research. (13th ed.), Wadsworth, Ohio: Cengage Learning.)
吳統雄(1990)。《電話調查:理論與方法》,台北:聯經。
吳蕙欣(2011)《結合多辭典與常識網路的情緒分析系統》臺灣大學資訊工程學研究所碩士論文。
黃厚銘、林意仁(2013)。《流動的群聚 (mob-ility): 網路起鬨的社會心理基礎》。〈新聞學研究〉,115: 1-50.
翁嫆琄(2014年9月16日)。〈蔡正元稱網友爆料PTT偏綠 網友大打臉〉,《新頭殼》。取自http://newtalk.tw/news/view/2014-09-16/51480
鍾泓良(2016年5月20日)。〈〈台北都會〉北市綠議員跟著連署 網友痛批扯後腿〉,《自由電子報》。取自http://news.ltn.com.tw/news/local/paper/991704
二、英文部分
Amichai-Hamburger, Y., Gazit, T., Bar-Ilan, J., Perez, O., Aharony, N., Bronstein, J., & Dyne, T. S. (2016). Psychological factors behind the lack of participation in online discussions. Computers in Human Behavior, 55, 268-277.
Anstead, N., & O`Loughlin, B. (2015). Social media analysis and public opinion: The 2010 UK general election. Journal of Computer‐Mediated Communication, 20(2), 204-220.
Arthur, C. (2006, July 20) What is the 1% rule? The Guardians. Retrieved from https://www.theguardian.com/technology/2006/jul/20/guardianweeklytechnologysection2
Agarwal, B., & Mittal, N. (2015). Prominent feature extraction for sentiment analysis. Springer.
Askay, D. A. (2015). Silence in the crowd: The spiral of silence contributing to the positive bias of opinions in an online review system. new media & society, 17(11), 1811-1829.
Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492-499). IEEE.
Barker, M., Barker, D. I., Bormann, N. F., & Zahay, D. (2016). Social Media Marketing. A Strategic Approach. (2nd ed.). Boston, MA: Cengage Learning.
Baumeister, R. F., Ainsworth, S. E., & Vohs, K. D. (2016). Are groups more or less than the sum of their members? The moderating role of individual identification. Behavioral and Brain Sciences, 39.
Chaovalit, P., & Zhou, L. (2005, January). Movie review mining: A comparison between supervised and unsupervised classification approaches. In System Sciences, 2005. HICSS`05. Proceedings of the 38th Annual Hawaii International Conference on (pp. 112c-112c). IEEE.
Chen, F., Zhang, L., & Latimer, J. (2014). How much has my co-worker contributed? The impact of anonymity and feedback on social loafing in asynchronous virtual collaboration. International Journal of Information Management, 34(5), 652-659.
Chiu, C. M., Hsu, M. H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision support systems, 42(3), 1872-1888.
Choi, J., Yang, M., & Chang, J. J. (2009). Elaboration of the hostile media phenomenon the roles of involvement, media skepticism, congruency of perceived media influence, and perceived opinion climate. Communication Research, 36(1), 54-75.
Culotta, A., & Cutler, J. (2016). Mining brand perceptions from twitter social networks. Marketing Science, 35(3), 343-362.
Curien, N., Fauchart, E., Laffond, G., & Moreau, F. (2006). Online consumer communities: Escaping the tragedy of the digital commons. na. Retrieved from: https://www.researchgate.net/profile/Francois_Moreau4/publication/281404035_Online_Consumer_Communities_Escaping_the_Tragedy_of_the_Digital_Commons/links/55e56d5808aede0b57359b42.pdf
Dave, K., Lawrence, S., & Pennock, D. M. (2003, May). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web (pp. 519-528). ACM.
Gearhart, S., & Zhang, W. (2015). Same spiral, different day? Testing the spiral of silence across issue types. Communication Research, 0093650215616456.
George, J. M. (1992). Extrinsic and intrinsic origins of perceived social loafing in organizations. Academy of Management Journal, 35(1), 191-202.
Glynn, C. F., & Park, E. (1997). REFERENCE GROUPS, OPINION INTENSITY, AND PUBLIC OPINION EXPRESSION. International Journal of Public Opinion Research, 9(3).
Greenwood, S., Perrin, A., & Duggan, M (2016) The Modern News Consumer: Social Media Update 2016. Retrieved from Pews Research Center Web site: http://www.pewinternet.org/2016/11/11/social-media-update-2016/
Gvirsman, S. D. (2015). Testing Our Quasi‐Statistical Sense: News Use, Political Knowledge, and False Projection. Political Psychology, 36(6), 729-747.
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process
analysis: A regression-based approach. New York, NY: The Guilford Press.
Hayes, A. F., Matthes, J., & Eveland Jr, W. P. (2013). Stimulating the quasi-statistical organ: Fear of social isolation motivates the quest for knowledge of the opinion climate. Communication Research, 40(4), 439-462.
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.
He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015). A novel social media competitive analytics framework with sentiment benchmarks. Information & Management, 52(7), 801-812.
Ho, S. S., & McLeod, D. M. (2008). Social-psychological influences on opinion expression in face-to-face and computer-mediated communication. Communication Research, 35(2), 190-207.
Ho, S. S., Chen, V. H. H., & Sim, C. C. (2013). The spiral of silence: examining how cultural predispositions, news attention, and opinion congruency relate to opinion expression. Asian Journal of Communication, 23(2), 113-134.
Karau, S. J., & Williams, K. D. (1993). Social loafing: A meta-analytic review and theoretical integration. Journal of personality and social psychology, 65(4), 681.
Khan, A. Z., Atique, M., & Thakare, V. M. (2015). Combining lexicon-based and learning-based methods for Twitter sentiment analysis. International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE), 89.
Kharde, V., & Sonawane, P. (2016). Sentiment analysis of twitter data: A survey of techniques. International Journal of Computer Applications, 139(11), 5-15.
Kim, S. H., Kim, H., & Oh, S. H. (2014). Talking about Genetically Modified (GM) foods in South Korea: The role of the Internet in the spiral of silence process. Mass Communication and Society, 17(5), 713-732.
Kouloumpis, E., Wilson, T., & Moore, J. D. (2011). Twitter sentiment analysis: The good the bad and the omg!. Icwsm, 11(538-541), 164.
Latané, B., Williams, K., & Harkins, S. (1979). Many hands make light the work: The causes and consequences of social loafing. Journal of personality and social psychology, 37(6), 822.
Larsson, A. O. (2011). Interactive to me–interactive to you? A study of use and appreciation of interactivity on Swedish newspaper websites. New Media & Society, 13(7), 1180-1197.
Lai, H. M., & Chen, T. T. (2014). Knowledge sharing in interest online communities: A comparison of posters and lurkers. Computers in Human Behavior, 35, 295-306.
Liao, S., & Chou, E. Y. (2012). Intention to adopt knowledge through virtual communities: posters vs lurkers. Online Information Review, 36(3), 442-461.
Lin, C., & He, Y. (2009, November). Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 375-384). ACM.
Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining text data (pp. 415-463). Springer US.
Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363-388.
Liu, Y., Bi, J. W., & Fan, Z. P. (2017). Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, 149-161.
Matthes, J., Hayes, A. F., Rojas, H., Shen, F., Min, S. J., & Dylko, I. B. (2012). Exemplifying a dispositional approach to cross-cultural spiral of silence research: Fear of social isolation and the inclination to self-censor. International Journal of Public Opinion Research, 24(3), 287-305.
Matthes, J. (2015). Observing the “spiral” in the spiral of silence. International Journal of Public Opinion Research, 27(2), 155-176.
McDevitt, M., Kiousis, S., & Wahl-Jorgensen, K. (2003). Spiral of moderation: Opinion expression in computer-mediated discussion. International Journal of Public Opinion Research, 15(4), 454-470.
Nair, M. (2011). Understanding and measuring the value of social media. Journal of Corporate Accounting & Finance, 22(3), 45-51.
Nekmat, E., & Gonzenbach, W. J. (2013). Multiple opinion climates in online forums: Role of website source reference and within-forum opinion congruency. Journalism & Mass Communication Quarterly, 90(4), 736-756.
Neuwirth, K., Frederick, E., & Mayo, C. (2007). The spiral of silence and fear of isolation. Journal of communication, 57(3), 450-468.
Neubaum, G., & Krämer, N. C. (2016). Monitoring the Opinion of the Crowd: Psychological Mechanisms Underlying Public Opinion Perceptions on Social Media. Media Psychology, 1-30.
Nonnecke, B., & Preece, J. (2001). Why lurkers lurk. AMCIS 2001 Proceedings, 294.
Nonnecke, B., & Preece, J. (2003). Silent participants: Getting to know lurkers better. In From usenet to CoWebs (pp. 110-132). Springer London.
Noelle‐Neumann, E. (1974). The spiral of silence a theory of public opinion. Journal of communication, 24(2), 43-51.
Noelle-Neumann, E. (1977). Turbulences in the climate of opinion: Methodological applications of the spiral of silence theory. Public Opinion Quarterly, (41), 143– 158.
Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Association for Computational Linguistics.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.
Pang, N., Ho, S. S., Zhang, A. M., Ko, J. S., Low, W. X., & Tan, K. S. (2016). Can spiral of silence and civility predict click speech on Facebook?. Computers in Human Behavior, 64, 898-905.
Paltoglou, G., & Thelwall, M. (2010, July). A study of information retrieval weighting schemes for sentiment analysis. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (pp. 1386-1395). Association for Computational Linguistics.
Plaks, J. E., & Higgins, E. T. (2000). Pragmatic use of stereotyping in teamwork: Social loafing and compensation as a function of inferred partner–situation fit. Journal of Personality and Social Psychology, 79(6), 962.
Powers, T., Advincula, D., Austin, M. S., Graiko, S., & Snyder, J. (2012). Digital and social media in the purchase decision process. Journal of advertising research, 52(4), 479-489.
Preece, J., Nonnecke, B., & Andrews, D. (2004). The top five reasons for lurking: improving community experiences for everyone. Computers in human behavior, 20(2), 201-223.
Price, K. H., Harrison, D. A., & Gavin, J. H. (2006). Withholding inputs in team contexts: member composition, interaction processes, evaluation structure, and social loafing. Journal of Applied Psychology, 91(6), 1375.
Ridings, C., Gefen, D., & Arinze, B. (2006). Psychological barriers: Lurker and poster motivation and behavior in online communities. Communications of the Association for Information Systems, 18(1), 16.
Scheufle, D. A., & Moy, P. (2000). Twenty-five years of the spiral of silence: A conceptual review and empirical outlook. International journal of public opinion research, 12(1), 3-28.
Scheufele, D. A., Shanahan, J., & Lee, E. (2001). Real talk manipulating the dependent variable in spiral of silence research. Communication research, 28(3), 304-324.
Shen, F., & Wang, T. (2015). Does perceived incongruence in opinion climate influence the degree of outspokenness? Evidence from two national events in China. Chinese Journal of Communication, 8(3), 253-271.
Shiue, Y. C., Chiu, C. M., & Chang, C. C. (2010). Exploring and mitigating social loafing in online communities. Computers in Human Behavior, 26(4), 768-777.
Singh, P. K., & Husain, M. S. (2014). Methodological study of opinion mining and sentiment analysis techniques. International Journal on Soft Computing, 5(1), 11.
Soroka, V., & Rafaeli, S. (2006, May). Invisible participants: how cultural capital relates to lurking behavior. In Proceedings of the 15th international conference on World Wide Web (pp. 163-172). ACM.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
Taboada, M. (2016). Sentiment analysis: an overview from linguistics. Annual Review of Linguistics, 2, 325-347.
Turney, P. D. (2002, July). Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 417-424). Association for Computational Linguistics.
Turney, P. & Littman, M. L. (2003) Measuring praise and criticism: inference of semantic orientation from association. ACM Trans Information System 21(4):315–346
Vallone, R. P., Ross, L., & Lepper, M. R. (1985). The hostile media phenomenon: biased perception and perceptions of media bias in coverage of the Beirut massacre. Journal of personality and social psychology, 49(3), 577.
Varathan, K. D., Giachanou, A., & Crestani, F. (2017). Comparative opinion mining: a review. Journal of the Association for Information Science and Technology, 68(4), 811-829.
Yeow, A., Johnson, S., & Faraj, S. (2006). Lurking: Legitimate or illegitimate peripheral participation?. ICIS 2006 Proceedings, 62.
Yun, W. G., & Park, S. Y. (2011). Selective posting: Willingness to post a message online. Journal of Computer‐Mediated Communication, 16(2), 201-227.
Yun, G. W., Park, S. Y., & Lee, S. (2016). Inside the spiral: Hostile media, minority perception, and willingness to speak out on a weblog. Computers in Human Behavior, 62, 236-243.
Zhang, B., & Vos, M. (2014). Social media monitoring: aims, methods, and challenges for international companies. Corporate Communications: An International Journal, 19(4), 371-383.
描述 碩士
國立政治大學
傳播學院傳播碩士學位學程
104464033
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104464033
資料類型 thesis
dc.contributor.advisor 張郁敏zh_TW
dc.contributor.author (Authors) 王嘉呈zh_TW
dc.creator (作者) 王嘉呈zh_TW
dc.date (日期) 2017en_US
dc.date.accessioned 1-Nov-2017 14:33:20 (UTC+8)-
dc.date.available 1-Nov-2017 14:33:20 (UTC+8)-
dc.date.issued (上傳時間) 1-Nov-2017 14:33:20 (UTC+8)-
dc.identifier (Other Identifiers) G0104464033en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/114307-
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) 104464033zh_TW
dc.description.abstract (摘要) 社群網站使用者不分年齡,幾乎沒有人不在這虛擬社交的浪潮上。儘管如此, 社群網站的交往卻不如現實般,社群中的絕大多數的內容是由少數發言者貢獻, 從來不發言的潛水者則佔了使用者基數的大部分。
本研究使用沈默螺旋理論的意見一致性概念與多種潛水動機作連接,藉此探 討發言者言論如何影響潛水者的動機選擇以及潛水行為表現。除此外,本研究藉 由同時使用社群聆聽技術和調查法作為研究方法,試圖以主、客觀區分兩種方法 並比較各自的益處和限制,也對社群聆聽技術只能使用發言者言論作為分析資料 來源的先天限制做出初步探討。
本研究收集到 599 份有效問卷和 285 篇社群網站文章,研究結果發現害怕被 孤立、社會性散漫兩種潛水動機完全中介了意見一致性對潛水行為的效果。主、 客觀研究方法的測量結果顯著相關,且對潛水動機之中介效果有相同預測能力。
zh_TW
dc.description.abstract (摘要) It is hard to find one had no experience using social networks in any age ranges. However, most of social network members are lurkers who barely post or comment to express their opinion. On the other hand, little regular posters contribute most content in every virtual society.
This study used the concept of opinion congruency in spiral of silence theory to link up multiple lurking motives found by past studies in order to clarify how posters’ texts influence lurking motives and behavior. Besides, for the purpose of comparing pros and cons between social listening and survey, this study adopted both research methods to measure major opinion in discussion threads wherea seprated the two methods into subjective and objective ones. Also, this study would have preliminary discuss about the fact of limited analytical source of social listening.
Collected 599 valid surveys and 285 social network discuss thread text, the result found that opinion congruency negatively influenced both lurking motives which positively influenced lurking behavior. The result also found that the subjective and objective research methods in this study were significantly related, and shared same predictive ability on both lurking motives’ mediated effect.
en_US
dc.description.tableofcontents 目錄 i
表目錄 iv
圖目錄 v
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第二章 文獻探討 4
第一節 潛水者與發言者概念型定義 4
第二節 意見一致性與潛水動機 7
一、 意見一致性 7
二、 意見一致性低與害怕被孤立動機 8
三、 意見一致性高與社會性散漫動機 9
第三節 調查法與社群聆聽技術 11
一、 調查法 11
二、 社群聆聽技術情緒分析法 13
三、 主、客觀方法區分與比較 15
第三章 研究方法 19
第一節 研究架構 19
第二節 社群與議題選擇 19
第三節 調查法 20
一、 母體、抽樣方法與流程 20
二、 問卷前測 21
三、 變項測量 22
(一) 主觀社群主流意見 22
(二) 受訪者意見 22
(三) 主觀社群意見一致性 22
(四) 害怕被孤立動機 23
(五) 社會性散漫動機 23
(六) 潛水行為 24
第四節 社群聆聽情緒分析法 25
一、 客觀社群主流意見分析流程 25
二、 客觀社群意見一致性 28
第四章 資料分析 29
第一節 問卷樣本 29
一、 調查法樣本輪廓 29
二、 問卷信度分析 29
三、 主觀社群意見一致性與潛水動機假設檢定 30
第二節 社群聆聽情緒分析法 32
一、 情緒分析技術前測 32
二、 社群聆聽情緒分析法樣本輪廓 34
三、 客觀社群意見一致性與潛水動機假設檢定 35
第五章 討論與建議 38
第一節 研究發現 38
一、 社群意見一致性、潛水動機、與潛水行為 38
二、 主、客觀社群主流意見 40
第二節 實務建議 40
第三節 研究限制與未來研究建議 41
參考文獻 44
附錄一、問卷 55
附錄二、影劇板情緒詞組列表(由亞洲指標數位行銷顧問股份有限公司提供) 59
zh_TW
dc.format.extent 6172699 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104464033en_US
dc.subject (關鍵詞) 潛水者zh_TW
dc.subject (關鍵詞) 潛水動機zh_TW
dc.subject (關鍵詞) 意見一致性zh_TW
dc.subject (關鍵詞) 沈默螺旋zh_TW
dc.subject (關鍵詞) 社群聆聽zh_TW
dc.subject (關鍵詞) 情緒分析zh_TW
dc.subject (關鍵詞) Lurkeren_US
dc.subject (關鍵詞) Lurker motivesen_US
dc.subject (關鍵詞) Opinion congruencyen_US
dc.subject (關鍵詞) Spiral of silenceen_US
dc.subject (關鍵詞) Social listeningen_US
dc.subject (關鍵詞) Sentiment analysisen_US
dc.title (題名) 意見一致性、潛水動機與潛水行為初探:社群聆聽技術與調查法之比較分析zh_TW
dc.title (題名) Exploring Relations among Opinion Congruency, Lurking Motives and Behavior: Social Listening versus Survey Methoden_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 一、中文部分
〈歷年個人及家庭上網行為趨勢分析〉(2016年7月)。取自台灣網路資訊中心http://www.twnic.net.tw/download/200307/20160922f.pdf
〈《網路社群調查》逾92%網友在討論區找購物資訊〉(2015年11月11日)。取自資策會產業情報研究所https://mic.iii.org.tw/IndustryObservations_PressRelease02.aspx?sqno=411
〈八成以上台灣人愛用Facebook、Line坐穩社群網站龍頭 1人平均擁4個社群帳號,年輕人更愛YouTube和IG〉(2017年5月1日)。取自資策會http://www.iii.org.tw/Press/NewsDtl.aspx?nsp_sqno=1934&fm_sqno=14
i-Buzz口碑研究中心(2017年3月22日)。〈論壇始祖PTT反映社會價值觀,後起之秀Dcard成年輕族群新據點〉,《動腦雜誌》。取自http://www.brain.com.tw/news/articlecontent?ID=44582&sort=#FOZw8s6e
李明穎 (2012)。〈網路潛水者的公民參與實踐之探索:以 「野草莓運動」為例〉,《新聞學研究》,112: 77-116。
林奇秀、陳一帆(2011)。〈淺析網路社群知識分享實證研究如何構思社會資本概念〉,《圖書資訊學刊》,9(2): 55-89。
林秀雲譯(2013)。《社會科學研究方法》,台北:聖智學習。(原書 Babbie, E. [2013]. The Practice of Social Research. (13th ed.), Wadsworth, Ohio: Cengage Learning.)
吳統雄(1990)。《電話調查:理論與方法》,台北:聯經。
吳蕙欣(2011)《結合多辭典與常識網路的情緒分析系統》臺灣大學資訊工程學研究所碩士論文。
黃厚銘、林意仁(2013)。《流動的群聚 (mob-ility): 網路起鬨的社會心理基礎》。〈新聞學研究〉,115: 1-50.
翁嫆琄(2014年9月16日)。〈蔡正元稱網友爆料PTT偏綠 網友大打臉〉,《新頭殼》。取自http://newtalk.tw/news/view/2014-09-16/51480
鍾泓良(2016年5月20日)。〈〈台北都會〉北市綠議員跟著連署 網友痛批扯後腿〉,《自由電子報》。取自http://news.ltn.com.tw/news/local/paper/991704
二、英文部分
Amichai-Hamburger, Y., Gazit, T., Bar-Ilan, J., Perez, O., Aharony, N., Bronstein, J., & Dyne, T. S. (2016). Psychological factors behind the lack of participation in online discussions. Computers in Human Behavior, 55, 268-277.
Anstead, N., & O`Loughlin, B. (2015). Social media analysis and public opinion: The 2010 UK general election. Journal of Computer‐Mediated Communication, 20(2), 204-220.
Arthur, C. (2006, July 20) What is the 1% rule? The Guardians. Retrieved from https://www.theguardian.com/technology/2006/jul/20/guardianweeklytechnologysection2
Agarwal, B., & Mittal, N. (2015). Prominent feature extraction for sentiment analysis. Springer.
Askay, D. A. (2015). Silence in the crowd: The spiral of silence contributing to the positive bias of opinions in an online review system. new media & society, 17(11), 1811-1829.
Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492-499). IEEE.
Barker, M., Barker, D. I., Bormann, N. F., & Zahay, D. (2016). Social Media Marketing. A Strategic Approach. (2nd ed.). Boston, MA: Cengage Learning.
Baumeister, R. F., Ainsworth, S. E., & Vohs, K. D. (2016). Are groups more or less than the sum of their members? The moderating role of individual identification. Behavioral and Brain Sciences, 39.
Chaovalit, P., & Zhou, L. (2005, January). Movie review mining: A comparison between supervised and unsupervised classification approaches. In System Sciences, 2005. HICSS`05. Proceedings of the 38th Annual Hawaii International Conference on (pp. 112c-112c). IEEE.
Chen, F., Zhang, L., & Latimer, J. (2014). How much has my co-worker contributed? The impact of anonymity and feedback on social loafing in asynchronous virtual collaboration. International Journal of Information Management, 34(5), 652-659.
Chiu, C. M., Hsu, M. H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision support systems, 42(3), 1872-1888.
Choi, J., Yang, M., & Chang, J. J. (2009). Elaboration of the hostile media phenomenon the roles of involvement, media skepticism, congruency of perceived media influence, and perceived opinion climate. Communication Research, 36(1), 54-75.
Culotta, A., & Cutler, J. (2016). Mining brand perceptions from twitter social networks. Marketing Science, 35(3), 343-362.
Curien, N., Fauchart, E., Laffond, G., & Moreau, F. (2006). Online consumer communities: Escaping the tragedy of the digital commons. na. Retrieved from: https://www.researchgate.net/profile/Francois_Moreau4/publication/281404035_Online_Consumer_Communities_Escaping_the_Tragedy_of_the_Digital_Commons/links/55e56d5808aede0b57359b42.pdf
Dave, K., Lawrence, S., & Pennock, D. M. (2003, May). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web (pp. 519-528). ACM.
Gearhart, S., & Zhang, W. (2015). Same spiral, different day? Testing the spiral of silence across issue types. Communication Research, 0093650215616456.
George, J. M. (1992). Extrinsic and intrinsic origins of perceived social loafing in organizations. Academy of Management Journal, 35(1), 191-202.
Glynn, C. F., & Park, E. (1997). REFERENCE GROUPS, OPINION INTENSITY, AND PUBLIC OPINION EXPRESSION. International Journal of Public Opinion Research, 9(3).
Greenwood, S., Perrin, A., & Duggan, M (2016) The Modern News Consumer: Social Media Update 2016. Retrieved from Pews Research Center Web site: http://www.pewinternet.org/2016/11/11/social-media-update-2016/
Gvirsman, S. D. (2015). Testing Our Quasi‐Statistical Sense: News Use, Political Knowledge, and False Projection. Political Psychology, 36(6), 729-747.
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process
analysis: A regression-based approach. New York, NY: The Guilford Press.
Hayes, A. F., Matthes, J., & Eveland Jr, W. P. (2013). Stimulating the quasi-statistical organ: Fear of social isolation motivates the quest for knowledge of the opinion climate. Communication Research, 40(4), 439-462.
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.
He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015). A novel social media competitive analytics framework with sentiment benchmarks. Information & Management, 52(7), 801-812.
Ho, S. S., & McLeod, D. M. (2008). Social-psychological influences on opinion expression in face-to-face and computer-mediated communication. Communication Research, 35(2), 190-207.
Ho, S. S., Chen, V. H. H., & Sim, C. C. (2013). The spiral of silence: examining how cultural predispositions, news attention, and opinion congruency relate to opinion expression. Asian Journal of Communication, 23(2), 113-134.
Karau, S. J., & Williams, K. D. (1993). Social loafing: A meta-analytic review and theoretical integration. Journal of personality and social psychology, 65(4), 681.
Khan, A. Z., Atique, M., & Thakare, V. M. (2015). Combining lexicon-based and learning-based methods for Twitter sentiment analysis. International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE), 89.
Kharde, V., & Sonawane, P. (2016). Sentiment analysis of twitter data: A survey of techniques. International Journal of Computer Applications, 139(11), 5-15.
Kim, S. H., Kim, H., & Oh, S. H. (2014). Talking about Genetically Modified (GM) foods in South Korea: The role of the Internet in the spiral of silence process. Mass Communication and Society, 17(5), 713-732.
Kouloumpis, E., Wilson, T., & Moore, J. D. (2011). Twitter sentiment analysis: The good the bad and the omg!. Icwsm, 11(538-541), 164.
Latané, B., Williams, K., & Harkins, S. (1979). Many hands make light the work: The causes and consequences of social loafing. Journal of personality and social psychology, 37(6), 822.
Larsson, A. O. (2011). Interactive to me–interactive to you? A study of use and appreciation of interactivity on Swedish newspaper websites. New Media & Society, 13(7), 1180-1197.
Lai, H. M., & Chen, T. T. (2014). Knowledge sharing in interest online communities: A comparison of posters and lurkers. Computers in Human Behavior, 35, 295-306.
Liao, S., & Chou, E. Y. (2012). Intention to adopt knowledge through virtual communities: posters vs lurkers. Online Information Review, 36(3), 442-461.
Lin, C., & He, Y. (2009, November). Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 375-384). ACM.
Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining text data (pp. 415-463). Springer US.
Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363-388.
Liu, Y., Bi, J. W., & Fan, Z. P. (2017). Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, 149-161.
Matthes, J., Hayes, A. F., Rojas, H., Shen, F., Min, S. J., & Dylko, I. B. (2012). Exemplifying a dispositional approach to cross-cultural spiral of silence research: Fear of social isolation and the inclination to self-censor. International Journal of Public Opinion Research, 24(3), 287-305.
Matthes, J. (2015). Observing the “spiral” in the spiral of silence. International Journal of Public Opinion Research, 27(2), 155-176.
McDevitt, M., Kiousis, S., & Wahl-Jorgensen, K. (2003). Spiral of moderation: Opinion expression in computer-mediated discussion. International Journal of Public Opinion Research, 15(4), 454-470.
Nair, M. (2011). Understanding and measuring the value of social media. Journal of Corporate Accounting & Finance, 22(3), 45-51.
Nekmat, E., & Gonzenbach, W. J. (2013). Multiple opinion climates in online forums: Role of website source reference and within-forum opinion congruency. Journalism & Mass Communication Quarterly, 90(4), 736-756.
Neuwirth, K., Frederick, E., & Mayo, C. (2007). The spiral of silence and fear of isolation. Journal of communication, 57(3), 450-468.
Neubaum, G., & Krämer, N. C. (2016). Monitoring the Opinion of the Crowd: Psychological Mechanisms Underlying Public Opinion Perceptions on Social Media. Media Psychology, 1-30.
Nonnecke, B., & Preece, J. (2001). Why lurkers lurk. AMCIS 2001 Proceedings, 294.
Nonnecke, B., & Preece, J. (2003). Silent participants: Getting to know lurkers better. In From usenet to CoWebs (pp. 110-132). Springer London.
Noelle‐Neumann, E. (1974). The spiral of silence a theory of public opinion. Journal of communication, 24(2), 43-51.
Noelle-Neumann, E. (1977). Turbulences in the climate of opinion: Methodological applications of the spiral of silence theory. Public Opinion Quarterly, (41), 143– 158.
Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Association for Computational Linguistics.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135.
Pang, N., Ho, S. S., Zhang, A. M., Ko, J. S., Low, W. X., & Tan, K. S. (2016). Can spiral of silence and civility predict click speech on Facebook?. Computers in Human Behavior, 64, 898-905.
Paltoglou, G., & Thelwall, M. (2010, July). A study of information retrieval weighting schemes for sentiment analysis. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (pp. 1386-1395). Association for Computational Linguistics.
Plaks, J. E., & Higgins, E. T. (2000). Pragmatic use of stereotyping in teamwork: Social loafing and compensation as a function of inferred partner–situation fit. Journal of Personality and Social Psychology, 79(6), 962.
Powers, T., Advincula, D., Austin, M. S., Graiko, S., & Snyder, J. (2012). Digital and social media in the purchase decision process. Journal of advertising research, 52(4), 479-489.
Preece, J., Nonnecke, B., & Andrews, D. (2004). The top five reasons for lurking: improving community experiences for everyone. Computers in human behavior, 20(2), 201-223.
Price, K. H., Harrison, D. A., & Gavin, J. H. (2006). Withholding inputs in team contexts: member composition, interaction processes, evaluation structure, and social loafing. Journal of Applied Psychology, 91(6), 1375.
Ridings, C., Gefen, D., & Arinze, B. (2006). Psychological barriers: Lurker and poster motivation and behavior in online communities. Communications of the Association for Information Systems, 18(1), 16.
Scheufle, D. A., & Moy, P. (2000). Twenty-five years of the spiral of silence: A conceptual review and empirical outlook. International journal of public opinion research, 12(1), 3-28.
Scheufele, D. A., Shanahan, J., & Lee, E. (2001). Real talk manipulating the dependent variable in spiral of silence research. Communication research, 28(3), 304-324.
Shen, F., & Wang, T. (2015). Does perceived incongruence in opinion climate influence the degree of outspokenness? Evidence from two national events in China. Chinese Journal of Communication, 8(3), 253-271.
Shiue, Y. C., Chiu, C. M., & Chang, C. C. (2010). Exploring and mitigating social loafing in online communities. Computers in Human Behavior, 26(4), 768-777.
Singh, P. K., & Husain, M. S. (2014). Methodological study of opinion mining and sentiment analysis techniques. International Journal on Soft Computing, 5(1), 11.
Soroka, V., & Rafaeli, S. (2006, May). Invisible participants: how cultural capital relates to lurking behavior. In Proceedings of the 15th international conference on World Wide Web (pp. 163-172). ACM.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
Taboada, M. (2016). Sentiment analysis: an overview from linguistics. Annual Review of Linguistics, 2, 325-347.
Turney, P. D. (2002, July). Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 417-424). Association for Computational Linguistics.
Turney, P. & Littman, M. L. (2003) Measuring praise and criticism: inference of semantic orientation from association. ACM Trans Information System 21(4):315–346
Vallone, R. P., Ross, L., & Lepper, M. R. (1985). The hostile media phenomenon: biased perception and perceptions of media bias in coverage of the Beirut massacre. Journal of personality and social psychology, 49(3), 577.
Varathan, K. D., Giachanou, A., & Crestani, F. (2017). Comparative opinion mining: a review. Journal of the Association for Information Science and Technology, 68(4), 811-829.
Yeow, A., Johnson, S., & Faraj, S. (2006). Lurking: Legitimate or illegitimate peripheral participation?. ICIS 2006 Proceedings, 62.
Yun, W. G., & Park, S. Y. (2011). Selective posting: Willingness to post a message online. Journal of Computer‐Mediated Communication, 16(2), 201-227.
Yun, G. W., Park, S. Y., & Lee, S. (2016). Inside the spiral: Hostile media, minority perception, and willingness to speak out on a weblog. Computers in Human Behavior, 62, 236-243.
Zhang, B., & Vos, M. (2014). Social media monitoring: aims, methods, and challenges for international companies. Corporate Communications: An International Journal, 19(4), 371-383.
zh_TW