近年來社群媒體興起，截至2021年為止全球已有超過一半的人口使用社群媒體，諸如推特、臉書、Instagram等等皆擁有相當多用戶，社群媒體更成為群眾發表意見以及提供建議的管道。因此社群媒體蘊含龐大資訊量，用戶的貼文、回覆、轉發及按讚，都包含文字及使用者行為等諸多資訊，特別當組織發生危機事件後，在社群媒體上會充斥著各式各樣的討論，後續研究也開始評估社群媒體作為危機管理工具的潛力。
本研究根據Austin等人於2012提出以社群媒體為中介之危機溝通模型出發，主要目的希望能夠透過文字探勘技術找出危機事件發生時充斥於社群媒體的資訊具何種特性、萃取使用者的重要特徵，及歸納整理意見領袖與群眾之間的話語互動關係以彌補過往理論較少著墨的人與人之間的互動關係，本研究使用推特資料集做為資料來源。
本研究主要採用文字探勘技術中的情緒分析及主題模型分析推特貼文內容，情緒分析使用基於辭典的情緒分類將正、負、中性三類區分出來，而使用GloVe結合LSTM訓練諷刺分類模型。主題模型則使用BERT衍伸的Bertopic進行群眾話題萃取。後續則以皮爾森績差相關係數研究不同特徵之間的正負向關係，最後以社會網路分析尋找意見領袖，並以意見領袖推文與群眾推文對照，了解以社群媒體為中介之危機溝通模型不同類群眾之話語引導關係。
研究結論發現諷刺作為一全新之危機歸因情緒，可作為後續研究者在危機管理領域之延伸研究內容。研究更發現此類推文有逐年遞增趨勢，且這一種類型推文因為容易轉換成轉推和喜歡數，因此認為此類推文較容易受到關注。歸納出事件發生後幫助危機管理的重要特徵，情緒分析結合發文地理位置幫助危機預判，並將危機事件中發文的用戶重新分類，找出在危機事件中願意理性討論的意見領袖及群眾並以此給予組織事件相關建議。整體而言，本研究擴充既有之SMCC模型，增加危機歸因情緒，也探討利益相關者之互動與引導關係。

In recent years, social media has emerged. As of 2021, more than half of the world's population has used social media, such as Twitter, Facebook, Instagram, etc., all have a considerable number of users, and social media has become a place for the masses to express opinions and provide suggestions 's pipeline. Therefore, social media contains a huge amount of information. Users’ posts, replies, forwarding, and likes all contain texts and user behaviors and many other information. Especially when a crisis occurs in an organization, social media will be flooded with various types of information. Various discussions, follow-up studies have also begun to assess the potential of social media as a crisis management tool.
This study is based on the crisis communication model mediated by social media proposed by Austin et al. in 2012. The main purpose of this study is to find out what characteristics of information flooding social media when crisis events occur, and to extract users through text mining technology. The important characteristics of , and summarize the discourse interaction between opinion leaders and the masses to make up for the interaction between people, which was less discussed in previous theories. This research uses the Twitter data set as the data source.
This research mainly uses sentiment analysis and topic model in text mining technology to analyze the content of Twitter posts. Sentiment analysis uses dictionary-based sentiment classification to distinguish positive, negative, and neutral categories, and uses GloVe combined with LSTM to train a sarcasm classification model . The topic model uses Bertopic derived from BERT for mass topic extraction. In the follow-up, the Pearson performance difference correlation coefficient is used to study the positive and negative relationship between different characteristics. Finally, social network analysis is used to find opinion leaders, and the tweets of opinion leaders are compared with mass tweets to understand the crisis mediated by social media. Communication Model Discourse-guided relationship of different kinds of masses.
The conclusion of the study found that irony, as a new crisis attribution emotion, can be used as an extension of the follow-up researcher in the field of crisis management. The research also found that such tweets have an increasing trend year by year, and because this type of tweets are easily converted into retweets and likes, it is believed that such tweets are more likely to receive attention. Summarize the important features that help crisis management after the event, sentiment analysis combined with the location of the post to help predict the crisis, reclassify the users who posted in the crisis event, find out the opinion leaders and the masses who are willing to discuss rationally in the crisis event, and use the This gives advice on organizing events. Overall, this study expands the existing SMCC model to increase crisis attribution sentiment, and also explores the interaction and guidance relationship of stakeholders.

一、中文文獻
1. 鍾偉軍, & 黃怡夢. (2016). 社交媒體與危機溝通理論的轉型: 從 SCCT 到 SMCC. 電子科技大學學報: 社會科學版, 18(5), 12-16.
2. 華碩小編之亂(2020年12月18日)。【華碩小編之亂】華碩社群小編自稱「中國公司」 台灣網友灌爆臉書嗆抵制。取自：https://www.mirrormedia.mg/story/20201218edi001/

二、英文文獻
1. Austin, L. L., & Jin, Y. (Eds.). (2018). Social media and crisis communication. New York: Routledge.
2. Agarwal, B., & Mittal, N. (2016). Machine learning approach for sentiment analysis. In Prominent feature extraction for sentiment analysis (pp. 21-45). Springer, Cham.
3. Austin, L., Fisher Liu, B., & Jin, Y. (2012). How audiences seek out crisis information: Exploring the social-mediated crisis communication model. Journal of applied communication research, 40(2), 188-207.
4. Abdalla, M., Alarabi, L., & Hendawi, A. (2021). Crisis management art from the risks to the control: a review of methods and directions. Information, 12(1), 18.
5. Alp, Z. Z., & Öğüdücü, Ş. G. (2018). Identifying topical influencers on twitter based on user behavior and network topology. Knowledge-Based Systems, 141,211-221.
6. Adamopoulos, P., Ghose, A., & Todri, V. (2018). The impact of user personality traits on word of mouth: Text-mining social media platforms. Information Systems Research, 29(3), 612-640.
7. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
8. Bundy, J., Pfarrer, M. D., Short, C. E., & Coombs, W. T. (2017). Crises and crisis management: Integration, interpretation, and research development. Journal of management, 43(6), 1661-1692.
9. Birch, J. (1994). New factors in crisis planning and response. Public Relations Quarterly, 39(1), 31.
10. Bukar, U. A., Jabar, M. A., Sidi, F., Nor, R. N. H., & Abdullah, S. (2021, July). Social Media Crisis Communication Model for Building Public Resilience: A Preliminary Study. In Business Information Systems (pp. 245-256).
11. Beigi, G., Hu, X., Maciejewski, R., & Liu, H. (2016). An overview of sentiment analysis in social media and its applications in disaster relief. Sentiment analysis and ontology engineering, 313-340.
12. Carr, C. T., & Hayes, R. A. (2015). Social media: Defining, developing, and divining. Atlantic journal of communication, 23(1), 46-65.
13. Coombs, W. T. (2007). Protecting organization reputations during a crisis: The development and application of situational crisis communication theory. Corporate reputation review, 10(3), 163-176.
14. Coombs, W. T., & Laufer, D. (2018). Global crisis management–current research and future directions. Journal of International Management, 24(3), 199-203.
15. Choi, Y., & Lin, Y. H. (2009). Consumer response to crisis: Exploring the concept of involvement in Mattel product recalls. Public Relations Review, 35(1), 18-22.
16. Chintalapudi, N., Battineni, G., Di Canio, M., Sagaro, G. G., & Amenta, F. (2021). Text mining with sentiment analysis on seafarers’ medical documents. International Journal of Information Management Data Insights, 1(1), 100005.
17. Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511-521.
18. Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. P. (2010, May). Measuring user influence in twitter: The million follower fallacy. In fourth international AAAI conference on weblogs and social media.
19. Dewi, F. K., Yudhoatmojo, S. B., & Budi, I. (2017, September). Identification of opinion leader on rumor spreading in online social network twitter using edge weighting and centrality measure weighting. In 2017 Twelfth International Conference on Digital Information Management (ICDIM) (pp. 313-318). IEEE.
20. Devika, M. D., Sunitha, C., & Ganesh, A. (2016). Sentiment analysis: a comparative study on different approaches. Procedia Computer Science, 87, 44-49.
21. de Fortuny, E. J., De Smedt, T., Martens, D., & Daelemans, W. (2012). Media coverage in times of political crisis: A text mining approach. Expert Systems with Applications, 39(14), 11616-11622.
22. Fink, S. L., Beak, J., & Taddeo, K. (1971). Organizational crisis and change. The Journal of applied behavioral science, 7(1), 15-37.
23. Ferrara, E., & Yang, Z. (2015). Quantifying the effect of sentiment on information diffusion in social media. PeerJ Computer Science, 1, e26.
24. Feuerriegel, S., & Gordon, J. (2018). Long-term stock index forecasting based on text mining of regulatory disclosures. Decision Support Systems, 112, 88-97.
25. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.
26. Gaikwad, S. V., Chaugule, A., & Patil, P. (2014). Text mining methods and techniques. International Journal of Computer Applications, 85(17).
27. Greco, F., & Polli, A. (2020). Emotional Text Mining: Customer profiling in brand management. International Journal of Information Management, 51, 101934.
28. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
29. Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI conference on web and social media (Vol. 8, No. 1, pp. 216-225).
30. Hatua, A., Nguyen, T. T., & Sung, A. H. (2017, December). Information diffusion on twitter: pattern recognition and prediction of volume, sentiment, and influence. In Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (pp. 157-167).
31. Hu, N., Zhang, T., Gao, B., & Bose, I. (2019). What do hotel customers complain about? Text analysis using structural topic model. Tourism Management, 72, 417-426.
32. 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.
33. Jin, Y., Liu, B. F., Anagondahalli, D., & Austin, L. (2014). Scale development for measuring publics’ emotions in crises. Public Relations Review, 40(3), 509-518.organizational
34. Jin, Y., & Liu, B. F. (2010). The blog-mediated crisis communication model: Recommendations for responding to influential external blogs. Journal of public relations research, 22(4), 429-455.
35. Jin, X., & Spence, P. R. (2021). Understanding crisis communication on social media with CERC: topic model analysis of tweets about Hurricane Maria. Journal of Risk Research, 24(10), 1266-1287.
36. Jung, Y., & Suh, Y. (2019). Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems, 123, 113074.
37. Kushwaha, A. K., Kar, A. K., & Dwivedi, Y. K. (2021). Applications of big data in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights, 1(2), 100017.
38. Karami, A., White, C. N., Ford, K., Swan, S., & Spinel, M. Y. (2020). Unwanted advances in higher education: Uncovering sexual harassment experiences in academia with text mining. Information Processing & Management, 57(2), 102167.
39. Kumar, B. S., & Ravi, V. (2016). A survey of the applications of text mining in financial domain. Knowledge-Based Systems, 114, 128-147.
40. Kumar, S., Kar, A. K., & Ilavarasan, P. V. (2021). Applications of text mining in services management: A systematic literature review. International Journal of Information Management Data Insights, 1(1), 100008.
41. Liu, B. F., Austin, L., & Jin, Y. (2011). How publics respond to crisis communication strategies: The interplay of information form and source. Public relations review, 37(4), 345-353.
42. Li, N., & Wu, D. D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision support systems, 48(2), 354-368.
43. Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META group research note, 6(70), 1.
44. Li, S. T., & Tsai, F. C. (2013). A fuzzy conceptualization model for text mining with application in opinion polarity classification. Knowledge-Based Systems, 39, 23-33.
45. Li, W., Chen, H., & Nunamaker Jr, J. F. (2016). Identifying and profiling key sellers in cyber carding community: AZSecure text mining system. Journal of Management Information Systems, 33(4), 1059-1086.
46. Martínez-Rojas, M., del Carmen Pardo-Ferreira, M., & Rubio-Romero, J. C. (2018). Twitter as a tool for the management and analysis of emergency situations: A systematic literature review. International Journal of Information Management, 43, 196-208.
47. Maier, S. (2010). All the news fit to post? Comparing news content on the web to newspapers, television, and radio. Journalism & Mass Communication Quarterly, 87(3-4), 548-562.
48. Mei, Y., Zhong, Y., & Yang, J. (2015, March). Finding and analyzing principal features for measuring user influence on Twitter. In 2015 IEEE First International Conference on Big Data Computing Service and Applications (pp. 478-486). IEEE.
49. Maheswari, M. U., & Sathiaseelan, J. G. R. (2017). Text mining: Survey on techniques and applications. International Journal of Science and Research, 6(6), 1660-1664.
50. Merendino, A., Dibb, S., Meadows, M., Quinn, L., Wilson, D., Simkin, L., & Canhoto, A. (2018). Big data, big decisions: The impact of big data on board level decision-making. Journal of Business Research, 93, 67-78.
51. Meel, P., & Vishwakarma, D. K. (2020). Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities. Expert Systems with Applications, 153, 112986.
52. Mitroff, I. I. (1994). Crisis management and environmentalism: A natural fit. California management review, 36(2), 101-113.
53. McInnes, L., Healy, J., & Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426.
54. McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. J. Open Source Softw., 2(11), 205.
55. Mak, A. K., & Song, A. O. (2019). Revisiting social-mediated crisis communication model: The Lancôme regenerative crisis after the Hong Kong Umbrella Movement. Public Relations Review, 45(4), 101812.
56. Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert systems with applications, 40(10), 4241-4251.
57. Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3), 1314-1324.
58. Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41(16), 7653-7670.
59. Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2015). Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment. Expert Systems with Applications, 42(1), 306-324.
60. Öztürk, N., & Ayvaz, S. (2018). Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics, 35(1), 136-147.
61. Ouaret, R., Birregah, B., Soulier, E., Auclair, S., & Boulahya, F. (2019, October). Random Forest location prediction from social networks during disaster events. In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) (pp. 535-540). IEEE.
62. Oberreuter, G., & Velásquez, J. D. (2013). Text mining applied to plagiarism detection: The use of words for detecting deviations in the writing style. Expert Systems with Applications, 40(9), 3756-3763.
63. Papadimitriou, C. H., Raghavan, P., Tamaki, H., & Vempala, S. (2000). Latent semantic indexing: A probabilistic analysis. Journal of Computer and System Sciences, 61(2), 217-235.
64. Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
65. Pejic-Bach, M., Bertoncel, T., Meško, M., & Krstić, Ž. (2020). Text mining of industry 4.0 job advertisements. International journal of information management, 50, 416-431.
66. Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information processing & management, 52(5), 949-975.
67. Reuter, C., Hughes, A. L., & Kaufhold, M. A. (2018). Social media in crisis management: An evaluation and analysis of crisis informatics research. International Journal of Human–Computer Interaction, 34(4), 280-294.
68. Roth-Cohen, O., & Avidar, R. (2022). A decade of social media in public relations research: A systematic review of published articles in 2010–2020. Public Relations Review, 48(1), 102154.
69. Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-based systems, 89, 14-46.
70. Rashid, J., Shah, S. M. A., & Irtaza, A. (2019). Fuzzy topic modeling approach for text mining over short text. Information Processing & Management, 56(6), 102060.
71. Ribeiro, A. C., Azevedo, B., Oliveira e Sá, J., & Baptista, A. A. (2020, September). How to measure influence in social networks?. In International Conference on Research Challenges in Information Science (pp. 38-57). Springer, Cham.
72. Simon, H. A. (1990). Bounded rationality. In Utility and probability (pp. 15-18). Palgrave Macmillan, London.
73. 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.
74. Sumathy, K. L., & Chidambaram, M. (2013). Text mining: concepts, applications, tools and issues-an overview. International Journal of Computer Applications, 80(4).
75. Shamim, S., Zeng, J., Shariq, S. M., & Khan, Z. (2019). Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information & Management, 56(6), 103135.
76. Sturges, D. L. (1994). Communicating through crisis: A strategy for organizational survival. Management communication quarterly, 7(3), 297-316.
77. Stephens, K. K., & Malone, P. C. (2009). If the organizations won't give us information…: The use of multiple new media for crisis technical translation and dialogue. Journal of public relations research, 21(2), 229-239.
78. Salehan, M., & Kim, D. (2020, January). An investigation of predictors of information diffusion in social media: Evidence from sentiment mining of Twitter messages. In Proceedings of the 53rd Hawaii International Conference on System Sciences.
79. Suarez-Tangil, G., Tapiador, J. E., Peris-Lopez, P., & Blasco, J. (2014). Dendroid: A text mining approach to analyzing and classifying code structures in android malware families. Expert Systems with Applications, 41(4), 1104-1117.
80. Seol, H., Lee, S., & Kim, C. (2011). Identifying new business areas using patent information: A DEA and text mining approach. Expert Systems with Applications, 38(4), 2933-2941.
81. Tan, A. H. (1999, April). Text mining: The state of the art and the challenges. In Proceedings of the pakdd 1999 workshop on knowledge disocovery from advanced databases (Vol. 8, pp. 65-70).
82. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307.
83. Vignal Lambret, C., & Barki, E. (2018). Social media crisis management: Aligning corporate response strategies with stakeholders’ emotions online. Journal of Contingencies and Crisis Management, 26(2), 295-305.
84. Wut, T. M., Xu, J. B., & Wong, S. M. (2021). Crisis management research (1985–2020) in the hospitality and tourism industry: A review and research agenda. Tourism Management, 85, 104307.
85. Xu, J. (2020). Does the medium matter? A meta-analysis on using social media vs. traditional media in crisis communication. Public relations review, 46(4), 101947.
86. Yang, S., Yao, J., & Qazi, A. (2020). Does the review deserve more helpfulness when its title resembles the content? Locating helpful reviews by text mining. Information Processing & Management, 57(2), 102179.
87. Yoon, J. (2012). Detecting weak signals for long-term business opportunities using text mining of Web news. Expert Systems with Applications, 39(16), 12543-12550.
88. Zheng, X., Han, J., & Sun, A. (2018). A survey of location prediction on twitter. IEEE Transactions on Knowledge and Data Engineering, 30(9), 1652-1671.
89. Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
90. Zhong, N., & Schweidel, D. A. (2020). Capturing changes in social media content: a multiple latent changepoint topic model. Marketing Science, 39(4), 827-846
91. Zhang, C., Lu, T., Chen, S., & Zhang, C. (2017). Integrating ego, homophily, and structural factors to measure user influence in online community. IEEE Transactions on Professional Communication, 60(3), 292-305.