社群媒體貼文策略研究-以台灣2021四大公投為例

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黃政豪(Cheng-Hao Huang) 查詢紙本館藏

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資訊管理學系在職專班

論文名稱

社群媒體貼文策略研究-以台灣2021四大公投為例
(Research on Social Media Message Strategy - A Case Study of Taiwan′s Four Major Referendums in 2021)

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至系統瀏覽論文 (2028-7-31以後開放)

摘要(中)

公民投票(Referendum)，簡稱公投，是公民(Citizens)對於提案、法律或政治問題的直接投票，為公民提供了在特定政策問題的決策過程中直接發言的可能性，也從而提高了後續政治決策的政治正當性。公投獲勝者的支持率會獲得顯著的提升，因此如何利用公投達成政治決策和提升政黨或個人影響力是個重要的課題。而早期的研究顯示，利用社群媒體來獲取資訊和表達政治觀點是個非常有效的工具，也有學者表示社群媒體是個用來接觸不同觀點的重要入口與管道。
本研究以台灣流量較大的社群媒體平台「PTT」和「Dcard」為對象，探究其中政治和公民投票相關的討論區中常見的貼文策略。研究結果顯示，「感性策略」中的「情緒感染」和「推廣策略」中的「資訊搜尋」策略方法在公投議題中具有顯著影響力，這與商業行銷策略研究的結果相符。感性策略利用情感內容激發社群媒體參與者的情緒，以達到支持或反對公投議題的目的。推廣策略則通過提供引人興趣的資訊，促使參與者積極參與公投活動。
感性策略和推廣策略的有效應用不僅適用於商業領域，同樣適用於政治領域。因此，建議政黨和意見領袖在確定自己對公投的立場和政治主張後，適時運用這些策略，以提高與網友互動和影響力，進而對未來的公共議題或選舉產生更多正面的影響。這樣的做法有助於增強支持者的情感共鳴，鼓勵他們更積極地參與相關議題的討論和投票。

摘要(英)

Referendum, also known as plebiscite, is a direct vote by citizens on proposals, laws, or political issues. It provides citizens with the opportunity to have a direct say in the decision-making process on specific policy issues, thereby enhancing the political legitimacy of subsequent political decisions. The winning side of a referendum gains a significant boost in support, hence utilizing referenda to achieve political decisions and elevate the influence of political parties or individuals is a crucial subject. Early studies have shown that the use of social media to access information and express political views is an extremely effective tool, and some scholars have suggested that social media is an important portal and channel for encountering different viewpoints.
This study focuses on two popular social media platforms in Taiwan, "PTT" and "Dcard", to explore common post strategies in their political and referendum-related discussion areas. The research findings reveal that the "emotional strategy" with the method of "emotional appeal" and the "promotional strategy" with the method of "information search" have significant influence in the context of referendum issues, which aligns with the results of studies in commercial marketing strategies. The emotional strategy uses emotional content to stir the emotions of social media participants to support or oppose referendum issues. The promotion strategy encourages participants to actively participate in referendum activities by providing intriguing information.
The effective application of emotional and promotional strategies is not only applicable in the business field but also in the political domain. Therefore, it is recommended that political parties and opinion leaders, after determining their positions and political claims on the referendum, use these strategies in a timely manner to increase interaction with netizens and their influence, thereby exerting more positive effects on future public issues or elections. This approach helps to enhance the emotional resonance of supporters, encouraging them to participate more actively in the discussion and voting on related issues.

關鍵字(中)

★ 社群媒體
★ 公投
★ 貼文策略
★ 自然語言處理
★ 情緒分析
★ 公共關係策略

關鍵字(英)

★ Social Media
★ Referendum
★ Posting Strategies
★ Natural Language Processing
★ Emotion Analysis
★ Public Relations Strategy

論文目次

中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
一、緒論 1
1-1 研究背景 1
1-2 研究動機 1
1-3 研究目的 4
1-4 研究問題 5
1-5 論文架構 5
二、文獻探討 7
2-1 社群媒體與政治參與 7
2-2 貼文策略 8
2-2-1 感性策略 12
2-2-2 知覺策略 12
2-2-3 推廣策略 13
2-3 社群媒體的貼文詞彙 14
2-4 自然語言處理 15
2-5 情緒分析 16
2-5-1 監督式機器學習法 17
2-5-2 字典法 17
2-6 公共關係策略 18
2-7 小結 19
三、研究方法 20
3-1 研究架構 20
3-2 研究對象 21
3-3 資料分析與處理 24
3-3-1 資料前處理 24
3-3-2 信度分析 32
3-3-3 情緒分析 35
3-3-4 內容分析 38
3-3-5 支持度分析 39
3-3-6 自然語言處理 40
四、結果與討論 43
4-1 貼文策略 43
4-1-1 貼文策略使用次數與網友回饋 43
4-1-2 影響力指標 52
4-1-3 詞彙分類結果 57
4-2 情緒分析 58
4-3 支持度分析 60
五、結論與建議 63
5-1 貼文策略 63
5-2 情緒分析 64
5-3 支持度分析 65
5-4 研究貢獻 67
六、研究限制與未來研究方向 69
6-1 研究對象 69
6-2 社群媒體 69
6-3 貼文策略種類 69
6-4 貼文與留言 70
6-5 貼文策略影響力 70
七、附件 71
7-1 四大公投說明 71
7-2 台灣全國性公民投票規定 73
7-3 公投結果 74
7-4 PTT及Dcard版規內容： 75
參考文獻 79

參考文獻

Abbasi, A., France, S., Zhang, Z., & Chen, H. (2010). Selecting attributes for sentiment classification using feature relation networks. IEEE Transactions on Knowledge and Data Engineering, 23(3), 447-462.
Abdous, M. h., & He, W. (2011). Using text mining to uncover students′ technology‐related problems in live video streaming. British journal of educational technology, 42(1), 40-49.
Abdous, M. h., Wu, H., & Yen, C.-J. (2012). Using data mining for predicting relationships between online question theme and final grade. Journal of Educational Technology & Society, 15(3), 77-88.
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. J. (2011). Sentiment analysis of twitter data. Proceedings of the workshop on language in social media (LSM 2011),
Aggarwal, R., Gopal, R., Gupta, A., & Singh, H. (2012). Putting money where the mouths are: The relation between venture financing and electronic word-of-mouth. Information Systems Research, 23(3-part-2), 976-992.
Ananiadou, S. (2009). National centre for text mining: introduction to tools for researchers (2008). In.
Ashley, C., & Tuten, T. (2015). Creative Strategies in Social Media Marketing: An Exploratory Study of Branded Social Content and Consumer Engagement. Psychology & marketing, 32(1), 15-27. https://doi.org/10.1002/mar.20761
Barbier, G., & Liu, H. (2011). Data mining in social media. In Social network data analytics (pp. 327-352). Springer.
Boase, J., & Kobayashi, T. (2008). Kei-Tying teens: Using mobile phone e-mail to bond, bridge, and break with social ties—a study of Japanese adolescents. International Journal of Human-Computer Studies, 66(12), 930-943. https://doi.org/10.1016/j.ijhcs.2008.07.004
Bode, L., Vraga, E. K., Borah, P., & Shah, D. V. (2014). A New Space for Political Behavior: Political Social Networking and its Democratic Consequences. Journal of Computer-Mediated Communication, 19(3), 414-429. https://doi.org/10.1111/jcc4.12048
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.
Bowman, S., & Willis, C. (2003). How audiences are shaping the future of news and information. T. M. C. a. t. A. P. Institute. https://wemedia.com/we-media-how-audiences-are-shaping-the-future-of-news-and-information/
Brown, B. P., Zablah, A. R., Bellenger, D. N., & Donthu, N. (2012). What factors influence buying center brand sensitivity? Industrial Marketing Management, 41(3), 508-520. https://doi.org/10.1016/j.indmarman.2011.06.008
Callcott, M. F., & Lee, W.-N. (1994). A Content Analysis of Animation and Animated Spokes-Characters in Television Commercials. Journal of advertising, 23(4), 1-12. https://doi.org/10.1080/00913367.1943.10673455
Cao, S. (2020). New Word Detection Algorithm Combining Correlation Confidence and Jieba Word Segmentation. Jisuanji Xitong Yingyong = Computer Systems and Applications(5), 144.
Castells, M. (2000). The rise of the network society (Vol. null).
Chaovalit, P., & Zhou, L. (2005). Movie review mining: A comparison between supervised and unsupervised classification approaches. Proceedings of the 38th annual Hawaii international conference on system sciences,
Chen, C.-M., Chen, Y.-T., & Liu, C.-Y. (2019). Development and evaluation of an automatic text annotation system for supporting digital humanities research. Library hi tech, 37(3), 436-455. https://doi.org/10.1108/LHT-10-2017-0219
Chen, H., & Zimbra, D. (2010). AI and opinion mining. IEEE Intelligent Systems, 25(3), 74-80.
Cheng, Y., Yu, Z., Hu, J., & Yang, M. (2022, 28-30 Oct. 2022). A Chinese Short Text Classification Method Based on TF-IDF and Gradient Boosting Decision Tree. 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML),
Chowdhury, K. H., Parvin, N., Weitenberner, C., & Becker, M. (2006). CONSUMER ATTITUDE TOWARD MOBILE ADVERTISING IN AN EMERGING MARKET: AN EMPIRICAL STUDY. International Journal of Mobile Marketing, 1.
De Pelsmacker, P., & Geuens, M. (1997). Emotional Appeals and Information Cues in Belgian Magazine Advertisements. International journal of advertising, 16(2), 123-147. https://doi.org/10.1080/02650487.1997.11104682
Dholakia, U. M. (2000). Temptation and resistance: An integrated model of consumption impulse formation and enactment. Psychology & marketing, 17(11), 955-982. https://doi.org/10.1002/1520-6793(200011)17:11<955::AID-MAR3>3.0.CO;2-J
Dommermuth, W. P. (1984). Promotion: Analysis, Creativity, and Strategy.
Ducoffe, R. H. (1996). Advertising value and advertising on the Web. Journal of Advertising Research, 36, 21-35.
Dunford, R., Su, Q., & Tamang, E. (2014). The pareto principle.
Esaiasson, P., Persson, M., Gilljam, M., & Lindholm, T. (2019). Reconsidering the Role of Procedures for Decision Acceptance. British Journal of Political Science, 49(1), 291-314. https://doi.org/10.1017/S0007123416000508
Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge university press.
Festinger, L. (1954). A theory of social comparison processes. Human relations, 7(2), 117-140.
Fleiss, J. L., Levin, B., & Paik, M. C. (2013). Statistical methods for rates and proportions. john wiley & sons.
Forgas, J. P. (2006). Affective influences on interpersonal behavior: Towards understanding the role of affect in everyday interactions. In Affect in social thinking and behavior. (pp. 269-289). Psychology Press.
Gil de Zúñiga, H., Molyneux, L., & Zheng, P. (2014). Social Media, Political Expression, and Political Participation: Panel Analysis of Lagged and Concurrent Relationships. Journal of communication, 64(4), 612-634. https://doi.org/10.1111/jcom.12103
Gil de Zúñiga, H., Jung, N., & Valenzuela, S. (2012). Social Media Use for News and Individuals′ Social Capital, Civic Engagement and Political Participation. Journal of Computer-Mediated Communication, 17(3), 319-336. https://doi.org/10.1111/j.1083-6101.2012.01574.x
Gilliland, D. I., & Johnston, W. J. (1997). Toward a model of business-to-business marketing communications effects. Industrial Marketing Management, 26(1), 15-29. https://doi.org/10.1016/S0019-8501(96)00032-6
Go, E., Jung, E. H., & Wu, M. (2014). The effects of source cues on online news perception. Computers in Human Behavior, 38, 358-367. https://doi.org/10.1016/j.chb.2014.05.044
Grimes, M., & Esaiasson, P. (2014). Political Research Quarterly, 67(4), 758.
Guo, J., Da Xu, L., Xiao, G., & Gong, Z. (2012). Improving multilingual semantic interoperation in cross-organizational enterprise systems through concept disambiguation. IEEE Transactions on Industrial Informatics, 8(3), 647-658.
Haghirian, P., Madlberger, M., & Tanuskova, A. (2005, 2005). Increasing Advertising Value of Mobile Marketing - An Empirical Study of Antecedents.
He, W. (2013). Improving user experience with case-based reasoning systems using text mining and Web 2.0. Expert Systems with Applications, 40(2), 500-507.
He, W., Chee, T., Chong, D., & Rasnick, E. (2012). Analyzing the trends of e-marketing from 2001 to 2010 with the use of bibliometrics and text mining. International Journal of Online Marketing, 2(1), 16-24.
Hedrick, S., & Gherghina, S. (2021). Why people vote in local level referendums: comparing Germany and the United States. European Politics and Society, 22(5), 716-732. https://doi.org/10.1080/23745118.2020.1820703
Heiser, R. S., Sierra, J. J., & Torres, I. M. (2008). Creativity Via Cartoon Spokespeople In Print Ads: Capitalizing on the Distinctiveness Effect. Journal of advertising, 37(4), 75-84. https://doi.org/10.2753/JOA0091-3367370406
Hsu, W., & Ye, Z. (2021). Social Media Message Strategy: An Exploratory Study into Managing Facebook Pages. International Journal of Innovation in Management, 9, 1-18.
Hung, J. l. (2012). Trends of e‐learning research from 2000 to 2008: Use of text mining and bibliometrics. British journal of educational technology, 43(1), 5-16.
Ingvaldsen, J. E., & Gulla, J. A. (2012). Industrial application of semantic process mining. Enterprise Information Systems, 6(2), 139-163.
Invernizzi Accetti, C., & Oskian, G. (2022). What Is a Consultative Referendum? The Democratic Legitimacy of Popular Consultations. Perspectives on Politics, 20(1), 123-138. https://doi.org/10.1017/S1537592720002340
Jain, L. C., Peng, S.-L., & Wang, S.-J. (2020). A Study of Text-Overlaid Image Compositions Information System About Enterprise Documents, Logo and Slogan. In (Vol. 1145, pp. 279-289). Springer International Publishing AG. https://doi.org/10.1007/978-3-030-46828-6_24
Johar, J. S., & Sirgy, M. J. (1991). Value-Expressive versus Utilitarian Advertising Appeals: When and Why to Use Which Appeal. Journal of advertising, 20(3), 23-33. https://doi.org/10.1080/00913367.1991.10673345
KEMP, S. (2022). DIGITAL 2022: OCTOBER GLOBAL STATSHOT REPORT. https://datareportal.com/reports/digital-2022-october-global-statshot
Kim, J.-S., & Kim, B.-S. (2018). Analysis of fire-accident factors using big-data analysis method for construction areas. KSCE Journal of Civil Engineering, 22(5), 1535-1543.
Kingdon, J. W. (1984). Agendas, Alternatives, and Public Policies. Boston: Little,Brown.
Klijn, E. H., & Koppenjan, J. F. M. (2000). Public Administration, 78(2), 365.
Kotler, P., & Keller, K. (2003). Marketing Management (11 ed.).
Larson, R. W., & Almeida, D. M. (1999). Emotional Transmission in the Daily Lives of Families: A New Paradigm for Studying Family Process. Journal of marriage and family, 61(1), 5-20. https://doi.org/10.2307/353879
Laskey, H. A., Day, E., & Crask, M. R. (1989). Typology of Main Message Strategies for Television Commercials. Journal of advertising, 18(1), 36-41. https://doi.org/10.1080/00913367.1989.10673141
Lavack, A. M., Thakor, M. V., & Bottausci, I. (2008). Music-brand congruency in highand low-cognition radio advertising. International journal of advertising, 27(4), 549-568. https://doi.org/10.2501/S0265048708080141
Li, L., Ge, R.-L., Zhou, S.-M., & Valerdi, R. (2012). Guest EditorialIntegrated Healthcare Information Systems. IEEE Transactions on Information Technology in Biomedicine, 16(4), 515-517.
Lin, F.-R., Hsieh, L.-S., & Chuang, F.-T. (2009). Discovering genres of online discussion threads via text mining. Computers & Education, 52(2), 481-495.
Linde, J., & Peters, Y. (2018). Responsiveness, support, and responsibility: How democratic responsiveness facilitates responsible government. Party Politics, 26(3), 291-304. https://doi.org/10.1177/1354068818763986
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.
Liu, Q., Zheng, Z., Zheng, J., Chen, Q., Liu, G., Chen, S., Chu, B., Zhu, H., Akinwunmi, B., Huang, J., Zhang, C. J. P., & Ming, W.-K. (2020). Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach. Journal of medical Internet research, 22(4), e19118. https://doi.org/10.2196/19118
Macafee, T., & De Simone, J. J. (2012). Killing the Bill Online? Pathways to Young People′s Protest Engagement via Social Media. Cyberpsychology, Behavior, and Social Networking, 15(11), 579-584. https://doi.org/10.1089/cyber.2012.0153
Macnamara, J., & Zerfass, A. (2012). Social media communication in organizations: The challenges of balancing openness, strategy, and management. International Journal of Strategic Communication, 6(4), 287-308.
McHugh, M. (2012). Interrater reliability: the kappa statistic. Biochemica Medica, 22 (3), 276–282. In.
McKnight, D. H., & Chervany, N. L. (2001). What Trust Means in E-Commerce Customer Relationships: An Interdisciplinary Conceptual Typology. International journal of electronic commerce, 6(2), 35-59. https://doi.org/10.1080/10864415.2001.11044235
McQuarrie, E. F., & Mick, D. G. (1996). Figures of Rhetoric in Advertising Language. The Journal of consumer research, 22(4), 424-438. https://doi.org/10.1086/209459
Meijer, A. J., & Torenvlied, R. (2016). The American Review of Public Administration, 46(2), 143.
Mergel, I. (2012). Social media in the public sector: A guide to participation, collaboration and transparency in the networked world (Vol. null).
Miao, Q., Schwarz, S., & Schwarz, G. (2021). Responding to COVID-19: Community volunteerism and coproduction in China. World Development, 137, 105128. https://doi.org/https://doi.org/10.1016/j.worlddev.2020.105128
Mishne, G., & Glance, N. S. (2006). Predicting movie sales from blogger sentiment. AAAI spring symposium: computational approaches to analyzing weblogs,
Mogensen, K. (2011). Thomas J. Johnson & David D. Perlmutter: New Media, Campaigning and the 2008 Facebook Election. New York: Routledge. 2011. MedieKultur: Journal of media and communication research, 28(53). https://doi.org/10.7146/mediekultur.v28i53.5715
Moreno, Á., Navarro, C., Tench, R., & Zerfass, A. (2015). Does social media usage matter? An analysis of online practices and digital media perceptions of communication practitioners in Europe. Public Relations Review, 41(2), 242-253.
Mudambi, S. M., Doyle, P., & Wong, V. (1997). An exploration of branding in industrial markets. Industrial Marketing Management, 26(5), 433-446. https://doi.org/10.1016/S0019-8501(96)00151-4
Oh, C., & Sheng, O. (2011). Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in information retrieval, 2(1–2), 1-135.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070.
Piron, F. (1991). DEFINING IMPULSE PURCHASING. Advances in consumer research, 18, 509-514.
Puri, R. (1996). Measuring and Modifying Consumer Impulsiveness: A Cost-Benefit Accessibility Framework. Journal of consumer psychology, 5(2), 87-113. https://doi.org/10.1207/s15327663jcp0502_01
Riloff, E., & Wiebe, J. (2003). Learning extraction patterns for subjective expressions. Proceedings of the 2003 conference on Empirical methods in natural language processing,
Robert, L. (2021). What Is Political Participation? Definition and Examples. https://www.thoughtco.com/political-participation-definition-examples-5198236
Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.
Scheffauer, R., Goyanes, M., & Gil de Zúñiga, H. (2021). Beyond social media news use algorithms: how political discussion and network heterogeneity clarify incidental news exposure. Online Information Review, 45(3), 633-650. https://doi.org/10.1108/OIR-04-2020-0133
statista.com. (2022). Daily number of unique visitors and total visits on PTT Bulletin Board System from June 2011 to June 2022. https://www.statista.com/statistics/1345506/taiwan-ptt-bulletin-board-system-daily-visits/
Stern, H. (1962). The Significance of Impulse Buying Today. Journal of marketing, 26(2), 59-62. https://doi.org/10.1177/002224296202600212
Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and Information Diffusion in Social Media-Sentiment of Microblogs and Sharing Behavior. Journal of management information systems, 29(4), 217-248. https://doi.org/10.2753/MIS0742-1222290408
Swani, K., Brown, B. P., & Milne, G. R. (2014). Should tweets differ for B2B and B2C? An analysis of Fortune 500 companies′ Twitter communications. Industrial Marketing Management, 43(5), 873-881. https://doi.org/10.1016/j.indmarman.2014.04.012
Swani, K., Milne, G. R., Brown, B. P., Assaf, A. G., & Donthu, N. (2017). What messages to post? Evaluating the popularity of social media communications in business versus consumer markets. Industrial Marketing Management, 62, 77-87. https://doi.org/https://doi.org/10.1016/j.indmarman.2016.07.006
Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of retailing, 77(2), 203-220. https://doi.org/10.1016/S0022-4359(01)00041-0
Tane, J., Schmitz, C., & Stumme, G. (2004). Semantic resource management for the web: an e-learning application. Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters,
Tsantis, L., & Castellani, J. (2001). Enhancing learning environments through solution-based knowledge discovery tools: Forecasting for self-perpetuating systemic reform. Journal of Special Education Technology, 16(4), 39-52.
Ünal, S., Ercis, A., & Keser, E. (2011). Attitudes towards Mobile Advertising – A Research to Determine the Differences between the Attitudes of Youth and Adults. PROCEEDINGS OF 7TH INTERNATIONAL STRATEGIC MANAGEMENT CONFERENCE, 24, 361-377. https://doi.org/10.1016/j.sbspro.2011.09.067 (Procedia Social and Behavioral Sciences)
Valenzuela, S., Arriagada, A., & Scherman, A. (2012). The Social Media Basis of Youth Protest Behavior: The Case of Chile. Journal of communication, 62(2), 299-314. https://doi.org/10.1111/j.1460-2466.2012.01635.x
van der Eijk, C., & Rose, J. (2020). Winner–loser effects in contentious constitutional referenda: Perceptions of procedural fairness and the Brexit referendum. The British Journal of Politics and International Relations, 23(1), 104-120. https://doi.org/10.1177/1369148120932852
Vrabel, J. K., Zeigler-Hill, V., & Shango, R. G. (2017). Spitefulness and humor styles. Personality and Individual Differences, 105, 238-243.
Webster, F. E. (1965). The "Deal-Prone" Consumer. Journal of Marketing Research, 2(2), 186-189. https://doi.org/10.2307/3149982
Wetzstein, B., Leitner, P., Rosenberg, F., Dustdar, S., & Leymann, F. (2011). Identifying influential factors of business process performance using dependency analysis. Enterprise Information Systems, 5(1), 79-98.
Wiki. (2022). Dcard. https://en.wikipedia.org/wiki/Dcard
Wu, L.-Y., Chen, K.-Y., Chen, P.-Y., & Cheng, S.-L. (2014). Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective. Journal of business research, 67(1), 2768-2776. https://doi.org/10.1016/j.jbusres.2012.09.007
Yang, H., Yu, J., Zo, H., & Choi, M. (2016). User acceptance of wearable devices: An extended perspective of perceived value. Telematics and Informatics, 33(2), 256-269. https://doi.org/https://doi.org/10.1016/j.tele.2015.08.007
Yue, Y., Zhang, Y., Xuegang, H., & Li, P. (2020). Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension. Journal of Physics: Conference Series, 1437, 012026. https://doi.org/10.1088/1742-6596/1437/1/012026
Zeithaml, V. A. (1988). CONSUMER PERCEPTIONS OF PRICE, QUALITY, AND VALUE - A MEANS-END MODEL AND SYNTHESIS OF EVIDENCE. Journal of marketing, 52(3), 2-22. https://doi.org/10.2307/1251446
Zerfass, A., Linke, A., Röttger, U., Berger, B. K., & Meng, J. (2014). Public relations leaders as sensemakers: A global study of leadership in public relations and communication management (Vol. null).
中華民國內政部統計處. (2018). 挖掘網路世界的文字寶藏－文字探勘與民意調查結合應用.
王力弘. (2015). 社群媒體新詞偵測系統以PTT八卦版為例國立政治大學]. 台北市. http://nccur.lib.nccu.edu.tw/handle/140.119/78817
王嵩音. (2016). 社交媒體政治性使用行為與公民參與之研究. 資訊社會研究, 32, 83-112.
江義平, 江孟璇, & 楊婉伶. (2019). 社群媒體使用行為之構形探究. Electronic Commerce Studies, 17(4).
李慶長, 張銀益, 陳文聰, & 蔡聰源. (2010). 關鍵字廣告之文案訊息設計對廣告成效影響的探討. 北商學報(18), 079-107. https://doi.org/10.29871/JNTCB.201007.0005
林彥伶. (2016). 社群媒體使用與異質性討論對政治參與的影響
The impact of social media and heterogeneous talk on political participation 國立政治大學]. 台北市. http://nccur.lib.nccu.edu.tw/handle/140.119/101055
林淑芳. (2018). 社群媒體與政治公民參與：網路政治討論頻率與政治討論異質性的中介角色 [Social Media and Political Civic Participation: The Mediating Role of Online Political Discussion and Discussion Heterogeneity]. 傳播與社會學刊(44), 25-48. https://doi.org/10.30180/CS.201804_(44).0003
唐淑珍. (2018). 論大數據下Facebook假新聞傳播現象. 文化創意產業研究學報, 8(3). https://doi.org/10.6639/JCCIR.201809_8(3).0002
徐幸瑜. (2020). 社群媒體時代下政策企業家促進政策變遷之策略研析：臺灣的以核養綠公投為例 [Strategies Employed by Policy Entrepreneurs to Promote Policy Change in the Era of Social Media: A Case Study of the “Go Green With Nuclear” Referendum in Taiwan*]. Taiwanese Journal of Political Science, 83, 101-148. https://doi.org/10.6166/TJPS.202003_(83).0003
張傳珩. (2019). 文本探勘與情緒分析於產品推薦之應用-以PTT電影版為例東吳大學]. 台北市.
陳致丞. (2019). 社群媒體粉絲專頁貼文策略研究 : 以醫療產業為例國立中央大學]. 桃園市中壢區.
曾淑芬. (2020). 社群媒體同溫層之傳播內容對社會建構之和諧與對立之挑戰. 財團法人台灣網路資訊中心. https://blog.twnic.tw/2020/12/25/16495/
程昕. (2019). Stylistic Study of Slogans of Beauty Products. 青春岁月(11), 82-83.
葉子筠. (2017). 社群媒體粉絲頁經營之研究 : 以Facebook某健康粉絲頁為例 = Message strategies for managing Facebook fan pages : An exploratory study 國立中央大學]. 桃園市中壢區.
翟治平, & 樊志育. (2002). 廣告設計學 = Creative advertising (初版 ed.). 揚智文化.
戴紹華. (2004). 應用聯合分析法於廣告文案設計之研究--以果汁飲料產品為例國立臺灣大學]. https://hdl.handle.net/11296/52b3tq

指導教授

許文錦

審核日期

2023-7-19

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