Introducing technological disruption: how breaking media attention on corporate events impacts online sentiment

References

• Abul-Fottouh, D. (2018). Brokerage roles and strategic positions in twitter networks of the 2011 Egyptian revolution: Twitter networks of the Egyptian revolution. Policy & Internet, 10(2), 218–240. https://doi.org/10.1002/poi3.169
   Web of Science ®Google Scholar
• Aldous, K. K., An, J., & Jansen, B. J. (2022). What really matters?: Characterising and predicting user engagement of news postings using multiple platforms, sentiments and topics. Behaviour and Information Technology, 1–24. https://doi.org/10.1080/0144929X.2022.2030798
   Web of Science ®Google Scholar
• Al-Natour, S., & Turetken, O. (2020). A comparative assessment of sentiment analysis and star ratings for consumer reviews. International Journal of Information Management, 54, 102132. https://doi.org/10.1016/j.ijinfomgt.2020.102132
   Web of Science ®Google Scholar
• Aversa, J., Jacobson, J., Hernandez, T., Cleave, E., Macdonald, M., & Dizonno, S. (2021). The social media response to the rollout of legalized cannabis retail in Ontario, Canada. Journal of Retailing and Consumer Services, 61(March), 102580. https://doi.org/10.1016/j.jretconser.2021.102580
   Google Scholar
• Barkemeyer, R., Faugère, C., Gergaud, O., & Preuss, L. (2020). Media attention to large-scale corporate scandals: Hype and boredom in the age of social media. Journal of Business Research, 109, 385–398. https://doi.org/10.1016/j.jbusres.2019.12.011
   Web of Science ®Google Scholar
• Berger, J., Kim, Y. D., Meyer, R., Inman, J. J., & Stephen, A. T. (2021). What makes content engaging? How emotional dynamics shape success. Journal of Consumer Research, 48(2), 235–250. https://doi.org/10.1093/jcr/ucab010
   Web of Science ®Google Scholar
• Bhanot, S. (2012). Use of social media by companies to reach their customers. SIES Journal of Management, 8(1), 47–55.
   Google Scholar
• Bhattacharya, D., & Ram, S. (2012). Sharing news articles using 140 characters: A diffusion analysis on Twitter. Paper presented at the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 966–971. https://doi.org/10.1109/ASONAM.2012.170
   Google Scholar
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
   Web of Science ®Google Scholar
• Bitektine, A., & Haack, P. (2015). The “macro” and the “micro” of legitimacy: Toward a multilevel theory of the legitimacy process. The Academy of Management Review, 40(1), 49–75. https://doi.org/10.5465/amr.2013.0318
   Web of Science ®Google Scholar
• Bogaert, M., Ballings, M., Van den Poel, D., & Oztekin, A. (2021). Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms. Decision Support Systems, 147, 113517. https://doi.org/10.1016/j.dss.2021.113517
   Web of Science ®Google Scholar
• Bollen, J., Mao, H., & Pepe, A. (2011). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the International AAAI Conference on Web and Social Media, Raleigh, North Carolina (Vol. 5, No. 1).
   Google Scholar
• Burmester, A. B., Becker, J. U., van Heerde, H. J., & Clement, M. (2015). The impact of pre-and post-launch publicity and advertising on new product sales. International Journal of Research in Marketing, 32(4), 408–417. https://doi.org/10.1016/j.ijresmar.2015.05.005
   Web of Science ®Google Scholar
• Castronovo, C., & Huang, L. (2012). Social media in an alternative marketing communication model. Journal of Marketing Development & Competitiveness, 6(1), 117–131. https://www.ezproxy.lib.ryerson.ca/login?url=https://www.proquest.com/scholarly-journals/social-media-alternative-marketing-communication/docview/1019047580/se-2?accountid=13631
   Google Scholar
• Chatterjee, S., Deng, S., Liu, J., Shan, R., & Jiao, W. (2018). Classifying facts and opinions in Twitter messages: A deep learning-based approach. Journal of Business Analytics, 1(1), 29–39. https://doi.org/10.1080/2573234X.2018.1506687
   Google Scholar
• Chen, X., Wang, S., Tang, Y., & Hao, T. (2019). A bibliometric analysis of event detection in social media. Online Information Review, 43(1), 29–52. https://doi.org/10.1108/oir-03-2018-0068
   Web of Science ®Google Scholar
• Chong, M. (2016). Sentiment analysis and topic extraction of the twitter network of #prayforparis. Proceedings of the Association for Information Science and Technology, 53(1), 1–4. https://doi.org/10.1002/pra2.2016.14505301133
   Google Scholar
• Christensen, C. M., Raynor, M. E., & McDonald, R. (2015). What is disruptive innovation. Harvard Business Review, 93(12), 44–53.
   Web of Science ®Google Scholar
• De, N. (2020, April 22). Libra’s long road from a Facebook lab to the global stage: A timeline. Retrieved October 25, 2020, from https://www.coindesk.com/libras-long-road-from-a-facebook-lab-to-the-global-stage-a-timeline
   Google Scholar
• De, N. (2023, May 11). Diem Confirms Shutdown as Silvergate Acquires the Project’s Assets. CoinDesk Latest Headlines RSS. https://www.coindesk.com/business/2022/01/31/silvergate-bank-confirms-diem-tech-acquisition/
   Google Scholar
• Diem Association. (2022). Vision Retrieved January 11, 2022, from https://www.diem.com/en-us/vision/
   Google Scholar
• Divine, G. W., Norton, H. J., Barón, A. E., & Juarez-Colunga, E. (2018). The wilcoxon-Mann-Whitney procedure fails as a test of medians. The American Statistician, 72(3), 278–286. https://doi.org/10.1080/00031305.2017.1305291
   Web of Science ®Google Scholar
• Duffy, C., & Fung, B. (2019). Mark Zuckerberg grilled by Congress over Libra and political ads policy. CNN Business.
   Google Scholar
• Erkan, I., & Evans, C. (2016). The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61, 47–55. https://doi.org/10.1016/j.chb.2016.03.003
   Web of Science ®Google Scholar
• Facebook. (October 29, 2020). Number of daily active Facebook users worldwide as of 3rd quarter 2020 (in millions) [graph]. In Statista. Retrieved January 24, 2021, from https://www-statista-com.ezproxy.lib.ryerson.ca/statistics/346167/facebook-global-dau/
   Google Scholar
• Fagerland, M. (2012). T- Tests, Non-parametric tests, and large studies—a paradox of statistical practice? BMC Medical Research Methodology, 12(1), 78. https://doi.org/10.1186/1471-2288-12-78
   PubMed Web of Science ®Google Scholar
• Fan, L., & Suh, Y. (2014). Why do users switch to a disruptive technology? an empirical study based on expectation-disconfirmation theory. Information & Management, 51(2), 240–248. https://doi.org/10.1016/j.im.2013.12.004
   Web of Science ®Google Scholar
• Frikha, W., Brahim, M., Jeribi, A., & Lahiani, A. (2023). COVID-19, russia-ukraine war and interconnectedness between stock and crypto markets: A wavelet-based analysis. Journal of Business Analytics, Ahead-Of-Print(ahead-Of-Print), 6(4), 255–275. https://doi.org/10.1080/2573234X.2023.2193224
   Google Scholar
• Garcia, R., & Calantone, R. (2002). A critical look at technological innovation typology and innovativeness terminology: A literature review. Journal of Product Innovation Management, 19(2), 110–132. https://doi.org/10.1016/S0737-6782(01)00132-1
   Web of Science ®Google Scholar
• Giachanou, A., & Crestani, F. (2016, July). Tracking sentiment by time series analysis. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, Pisa, Italy (pp. 1037–1040).
   Google Scholar
• Godes, D., & Silva, J. C. (2012). Sequential and temporal dynamics of online opinion. Marketing Science, 31(3), 448–473. https://doi.org/10.1287/mksc.1110.0653
   Web of Science ®Google Scholar
• Google Cloud Natural Language API Basics. (2020). Retrieved November 29, 2020, from https://cloud.google.com/natural-language/docs/basics#sentiment-analysis-values
   Google Scholar
• Gruzd, A. (2016). Netlytic: Software for Automated Text and Social Network Analysis. Available at http://Netlytic.org
   Google Scholar
• Gruzd, A., Paulin, D., & Haythornthwaite, C. (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. https://doi.org/10.18608/jla.2016.33.4
   Google Scholar
• Herrera-Contreras, A. A., Sánchez-Delacruz, E., & Meza-Ruiz, I. V. (2020, December). Twitter opinion analysis about topic 5g technology. In International conference on applied technologies (pp. 191–203). Springer, Cham. https://doi.org/10.1007/978-3-030-42517-3_15
   Google Scholar
• Holsapple, C. W., Hsiao, S. H., & Pakath, R. (2018). Business social media analytics: Characterization and conceptual framework. Decision Support Systems, 110(August 2016), 32–45. https://doi.org/10.1016/j.dss.2018.03.004
   Google Scholar
• Hou, F., Li, B., Chong, A. Y. L., Yannopoulou, N., & Liu, M. J. (2017). Understanding and predicting what influence online product sales? A neural network approach. Production Planning and Control, 28(11–12), 964–975. https://doi.org/10.1080/09537287.2017.1336791
   Web of Science ®Google Scholar
• Ibrahim, N. F., & Wang, X. (2019). A text analytics approach for online retailing service improvement: Evidence from Twitter. Decision Support Systems, 121, 37–50. https://doi.org/10.1016/j.dss.2019.03.002
   Web of Science ®Google Scholar
• Jacobson, J., Gruzd, A., & Hernández-García, Á. (2020). Social media marketing: Who is watching the watchers? Journal of Retailing and Consumer Services, 53, 101774. https://doi.org/10.1016/j.jretconser.2019.03.001
   Web of Science ®Google Scholar
• Jeong, B., Yoon, J., & Lee, J. M. (2019). Social media mining for product planning: A product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management, 48(April 2017), 280–290. https://doi.org/10.1016/j.ijinfomgt.2017.09.009
   Google Scholar
• Jiang, J. J., & Klein, G. (2009). Expectation-confirmation theory: Capitalizing on descriptive power. Handbook of Research on Contemporary Theoretical Models in Information Systems, 384–401. https://doi.org/10.4018/978-1-60566-659-4.ch022
   Google Scholar
• Jiang, X., Su, M.-H., Hwang, J., Lian, R., Brauer, M., Kim, S., & Shah, D. (2021). Polarization over vaccination: Ideological differences in Twitter expression about COVID-19 vaccine favorability and specific hesitancy concerns. Social Media + Society, 7(3), 7(3. https://doi.org/10.1177/20563051211048413
   PubMed Web of Science ®Google Scholar
• Kazmaier, J., & van Vuuren, J. H. (2020). A generic framework for sentiment analysis: Leveraging opinion-bearing data to inform decision making. Decision Support Systems, 135, 113304. https://doi.org/10.1016/j.dss.2020.113304
   Web of Science ®Google Scholar
• Kim, K., Yoon, S., & Choi, Y. K. (2019). The effects of eWOM volume and valence on product sales–an empirical examination of the movie industry. International Journal of Advertising, 38(3), 471–488. https://doi.org/10.1080/02650487.2018.1535225
   Web of Science ®Google Scholar
• Kiousis, S. (2004). Explicating media salience: A factor analysis of new york times issue coverage during the 2000 U.S. presidential election. Journal of Communication, 54(1), 71–87. https://doi.org/10.1111/j.1460-2466.2004.tb02614.x
   Web of Science ®Google Scholar
• Lehmann, J., Castillo, C., Lalmas, M., & Zuckerman, E. (2013). Transient news crowds in social media. Proceedings of the International AAAI Conference on Web and Social Media, 7 (1), 351–360. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14423
   Google Scholar
• Li, J., Haider, Z. A., Jin, X., & Yuan, W. (2019). Corporate controversy, social responsibility and market performance: International evidence. Journal of International Financial Markets, Institutions and Money, 60, 1–18. https://doi.org/10.1016/j.intfin.2018.11.013
   Web of Science ®Google Scholar
• Lingiardi, V., Carone, N., Semeraro, G., Musto, C., D’Amico, M., & Brena, S. (2020). Mapping twitter hate speech towards social and sexual minorities: A lexicon-based approach to semantic content analysis. Behaviour and Information Technology, 39(7), 711–721. https://doi.org/10.1080/0144929X.2019.1607903
   Web of Science ®Google Scholar
• Lipizzi, C., Iandoli, L., & Ramirez Marquez, J. E. (2015). Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams. International Journal of Information Management, 35(4), 490–503. https://doi.org/10.1016/j.ijinfomgt.2015.04.001
   Web of Science ®Google Scholar
• Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60. https://doi.org/10.1214/aoms/1177730491
   Google Scholar
• Marres, N., & Moats, D. (2015). Mapping controversies with social media: The case for symmetry. Social Media + Society, 1(2), 1(2. https://doi.org/10.1177/2056305115604176
   Web of Science ®Google Scholar
• Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
   Google Scholar
• Meiselman, H. L. (2015). A review of the current state of emotion research in product development. Food Research International, 76, 192–199. https://doi.org/10.1016/j.foodres.2015.04.015
   Web of Science ®Google Scholar
• Mention, A. L., Barlatier, P. J., & Josserand, E. (2019). Using social media to leverage and develop dynamic capabilities for innovation. Technological Forecasting and Social Change, 144(March), 242–250. https://doi.org/10.1016/j.techfore.2019.03.003
   Google Scholar
• Nguyen, L. T., Wu, P., Chan, W., Peng, W., & Zhang, Y. (2012, August). Predicting collective sentiment dynamics from time-series social media. In Proceedings of the first international workshop on issues of sentiment discovery and opinion mining, Beijing, China (pp. 1–8).
   Google Scholar
• Nisar, T. M., Prabhakar, G., Ilavarasan, P. V., & Baabdullah, A. M. (2020). Up the ante: Electronic word of mouth and its effects on firm reputation and performance. Journal of Retailing & Consumer Services, 53(January 2019), 101726. https://doi.org/10.1016/j.jretconser.2018.12.010
   Google Scholar
• Noor, H., & Turetken, O. (2023) “What drives sentiments on social media? An exploratory study on the 2021 Canadian federal election” In Proceedings of Hawaii International Conference on System Sciences (HICSS-56), January 3 – 6, 2023, Maui, HI
   Google Scholar
• Ouyang, Z., Wei, J., Xiao, Y., & Wang, F. (2017). Media attention and corporate disaster relief: Evidence from China. Disaster Prevention and Management, 26(1), 2–12. https://doi.org/10.1108/DPM-10-2015-0247
   Web of Science ®Google Scholar
• Pallas, F., Staufer, D., & Kuhlenkamp, J. (2020). Evaluating the accuracy of cloud NLP services using ground-truth experiments. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 341–350). IEEE. https://doi.org/10.1109/BigData50022.2020.9378188.
   Google Scholar
• Park, C. S. (2019). Does Too much news on social media Discourage news Seeking? Mediating role of news efficacy between perceived news overload and news avoidance on social media. Social Media + Society, 5(3), 205630511987295. https://doi.org/10.1177/2056305119872956
   Web of Science ®Google Scholar
• Park, C. W., & Seo, D. R. (2018). Sentiment analysis of Twitter corpus related to artificial intelligence assistants. 2018 5th International Conference on Industrial Engineering and Applications, ICIEA 2018, Singapore (pp. 495–498).
   Google Scholar
• Prasad Mishra, D., & Bhabra, H. S. (2001). Assessing the economic worth of new product pre-announcement signals: Theory and empirical evidence. Journal of Product & Brand Management, 10(2), 75–93. https://doi.org/10.1108/10610420110388645
   Google Scholar
• Rathore, A. K., & Ilavarasan, P. V. (2020). Pre- and post-launch emotions in new product development: Insights from Twitter analytics of three products. International Journal of Information Management, 50, 111–127. https://doi.org/10.1016/j.ijinfomgt.2019.05.015
   Web of Science ®Google Scholar
• Reis, J., Benevenuto, F., Olmo, P., Prates, R., Kwak, H., & An, J. (2015). Breaking the news: First impressions matter on online news. Proceedings of the 9th International Conference on Web and Social Media, ICWSM 2015, Oxford, United Kingdom (pp. 357–366).
   Google Scholar
• Reuter, C., Stieglitz, S., & Imran, M. (2020). Social media in conflicts and crises. Behaviour and Information Technology, 39(3), 241–251. https://doi.org/10.1080/0144929X.2019.1629025
   Web of Science ®Google Scholar
• Rogers, E. M. (2003). Diffusion of innovations. ( (Fifth; Free Press trade paperback ed.)). Free Press.
   Google Scholar
• Ruiz-Soler, J., Curini, L., & Ceron, A. (2019). Commenting on political topics through Twitter: Is European Politics European? Social Media + Society, 5(4), 205630511989088. https://doi.org/10.1177/2056305119890882
   Web of Science ®Google Scholar
• Schatzel, K., & Calantone, R. (2006). Creating market anticipation: An exploratory examination of the effect of preannouncement behavior on a new product’s launch. Journal of the Academy of Marketing Science, 34(3), 357–366. https://doi.org/10.1177/0092070304270737
   Web of Science ®Google Scholar
• Schumaker, R. P., Jarmoszko, A. T., & Labedz, C. S. (2016). Predicting wins and spread in the premier league using a sentiment analysis of twitter. Decision Support Systems, 88, 76–84. https://doi.org/10.1016/j.dss.2016.05.010
   Web of Science ®Google Scholar
• Singh Chauhan, S., Srinivasan, K., & Sharma, T. (2022). A trans-national comparison of stock market movements and related social media chatter during the COVID-19 pandemic. Journal of Business Analytics, 6(3), ahead-of-print(ahead-of-print). 1–14. https://doi.org/10.1080/2573234X.2022.2155257
   Google Scholar
• Sorescu, A., Warren, N. L., & Ertekin, L. (2017). Event study methodology in the marketing literature: An overview. Journal of the Academy of Marketing Science, 45(2), 186–207. https://doi.org/10.1007/s11747-017-0516-y
   Web of Science ®Google Scholar
• Sustainalytics. 2014. Research Methodology. Retrieved December 16th, 2020 from https://www.sustainalytics.com/controversies-research/#1482352879777-ebfb8fdd-0440
   Google Scholar
• Szczech, M., & Turetken, O. (2018). The competitive landscape of mobile communications industry in Canada: Predictive analytic modeling with Google trends and Twitter. In A. V. Deokar, A. V. Gupta, L. S. Iyer, & Jones M. C., (Eds.), Analytics and data science (pp. 143–162). Springer.
   Google Scholar
• Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406–418. https://doi.org/10.1002/asi.21462
   Web of Science ®Google Scholar
• Tripathi, S., Deokar, A., & Ajjan, H. (2020) “Understanding the order effect of online review sentiments and product features” Pre-ICIS SIGDSA Symposium “Addressing Global and Grand Challenges with Analytics,” Hyderabad, India, December 2020 (pp. 12–13).
   Google Scholar
• van Lent, L. G. G., Sungur, H., Kunneman, F. A., van de Velde, B., & Das, E. (2017). Too far to care? measuring public attention and fear for ebola using Twitter. Journal of Medical Internet Research, 19(6), e193. https://doi.org/10.2196/jmir.7219
   PubMed Web of Science ®Google Scholar
• Vassilikopoulou, A., Siomkos, G., Chatzipanagiotou, K., & Pantouvakis, A. (2009). Product-harm crisis management: Time heals all wounds? Journal of Retailing and Consumer Services, 16(3), 174–180. https://doi.org/10.1016/j.jretconser.2008.11.011
   Google Scholar
• Verma, S., & Yadav, N. (2021). Past, present, and future of electronic word of mouth (EWOM). Journal of Interactive Marketing, 53, 111–128. https://doi.org/10.1016/j.intmar.2020.07.001
   Web of Science ®Google Scholar
• Weng, J., & Lee, B.-S. (2011). Event detection in Twitter. Proceedings of the International AAAI Conference on Web and Social Media, 5 (1), 401–408. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14102
   Google Scholar
• Wheelwright, S. C., & Clark, K. B. (1992). Creating project plans to focus product development. Harvard Business Review, 70(2), 70–82.
   PubMed Web of Science ®Google Scholar
• Workman, M. D. (2021). Antecedents of social media activism: An analytical study of a controversial topic. Behaviour and Information Technology, 40(3), 300–310. https://doi.org/10.1080/0144929X.2019.1687755
   Web of Science ®Google Scholar
• Wu, B., & Shen, H. (2015). Analyzing and predicting news popularity on Twitter. International Journal of Information Management, 35(6), 702–711. https://doi.org/10.1016/j.ijinfomgt.2015.07.003
   Web of Science ®Google Scholar
• Yadav, S., Kaushik, A., Sharma, M., & Sharma, S. (2022). Disruptive technologies in smart farming: An expanded view with sentiment analysis. AgriEngineering, 4(2), 424–460. https://doi.org/10.3390/agriengineering4020029
   Google Scholar
• Zhigang, W., Lei, Z., & Xintao, L. (2020). Consumer response to corporate hypocrisy from the perspective of expectation confirmation theory. Frontiers in Psychology, 11(November). https://doi.org/10.3389/fpsyg.2020.580114
   PubMedGoogle Scholar
• Zimmerman, D. W. (1987). Comparative power of student T-test and Mann-Whitney U test for unequal sample sizes and variances. The Journal of Experimental Education, 55(3), 171–174. https://doi.org/10.1080/00220973.1987.10806451
   Web of Science ®Google Scholar
• Zolbanin, H. M., & Wynn, D. (2023). From star rating to sentiment rating: Using textual content of online reviews to develop more effective reputation systems for peer-to-peer accommodation platforms. Journal of Business Analytics, 6(2), 127–139. https://doi.org/10.1080/2573234X.2022.2122880
   Google Scholar
• Zyglidopoulos, S. C., Georgiadis, A. P., Carroll, C. E., & Siegel, D. S. (2012). Does media attention drive corporate social responsibility? Journal of Business Research, 65(11), 1622–1627. https://doi.org/10.1016/j.jbusres.2011.10.021
   Web of Science ®Google Scholar