References
- Ainin, S., Parveen, F., Moghavvemi, S., Jaafar, N. I., & Mohd Shuib, N. L. (2015). Factors influencing the use of social media by SMEs and its performance outcomes. Industrial Management & Data Systems, 115(3), 570–588. https://doi.org/10.1108/IMDS-07-2014-0205
- Assenov, Y., Ramírez, F., Schelhorn, S.-E., Lengauer, T., & Albrecht, M. (2008). Computing topological parameters of biological networks. Bioinformatics (oxford, England), 24(2), 282–284. https://doi.org/10.1093/bioinformatics/btm554
- Bennett, W. L., & Segerberg, A. (2012). The logic of connective action: Digital media and the personalization of contentious politics. Information, Communication & Society, 15(5), 739–768. https://doi.org/10.1080/1369118X.2012.670661
- Bird, D. K., Haynes, K., van den Honert, R., McAneney, J., & Poortinga, W. (2014). Nuclear power in Australia: A comparative analysis of public opinion regarding climate change and the Fukushima disaster. Energy Policy, 65, 644–653. https://doi.org/10.1016/j.enpol.2013.09.047
- Bolsover, G., & Howard, P. (2018). Chinese computational propaganda: Automation, algorithms and the manipulation of information about chinese politics on twitter and weibo. In History and nationalist legitimacy in contemporary China (Vol. 14, pp. 2063–2080). Springer.https://doi.org/10.1080/1369118X.2018.1476576
- Carley, K. M., Malik, M., Landwehr, P. M., Pfeffer, J., & Kowalchuck, M. (2016). Crowd sourcing disaster management: The complex nature of Twitter usage in Padang Indonesia. Safety Science, 90, 48–61. https://doi.org/10.1016/j.ssci.2016.04.002
- Chao, E. (2017). Reconfiguring class, gender, ethnicity and ethics in Chinese Internet culture. Journal of International and Global Studies, 9, 182–185. https://doi.org/10.4324/9781315668154
- Cho, M., Schweickart, T., & Haase, A. (2014). Public engagement with nonprofit organizations on Facebook. Public Relations Review, 40(3), 565–567. https://doi.org/10.1016/j.pubrev.2014.01.008
- Dixon, L. J., Correa, T., Straubhaar, J., Covarrubias, L., Graber, D., Spence, J., & Rojas, V. (2014). Gendered space: The digital divide between male and female users in internet public access sites. Journal of Computer-Mediated Communication, 19(4), 991–1009. https://doi.org/10.1111/jcc4.12088
- Erikson, R. S., & Tedin, K. L. (2015). American public opinion: Its origins, content and impact. Routledge.
- Feng, W., Wang, H., Xu, K., Wu, J., & Jia, X. (2013, July 08-11). Characterizing information diffusion in online social networks with linear diffusive model. 2013 IEEE 33rd international conference on Distributed Computing Systems.
- Finseraas, H. (2009). Income inequality and demand for redistribution: A multilevel analysis of European public opinion. Scandinavian Political Studies, 32(1), 94–119. https://doi.org/10.1111/j.1467-9477.2008.00211.x
- Fischer-Preßler, D., Schwemmer, C., & Fischbach, K. (2019). Collective sense-making in times of crisis: Connecting terror management theory with Twitter user reactions to the Berlin terrorist attack. Computers in Human Behavior, 100, 138–151. https://doi.org/10.1016/j.chb.2019.05.012
- Foroozani, A., & Ebrahimi, M. (2019). Anomalous information diffusion in social networks: Twitter and Digg. Expert Systems with Applications, 134, 249–266. https://doi.org/10.1016/j.eswa.2019.05.047
- Fox, C. S., Gurary, E. B., Ryan, J., Bonaca, M., Barry, K., Loscalzo, J., & Massaro, J. (2016). Randomized controlled trial of social media: Effect of increased intensity of the intervention. Journal of the American Heart Association, 5, e003088. https://doi.org/10.1161/jaha.115.003088
- Freeman, L. C., Roeder, D., & Mulholland, R. R. (1979). Centrality in social networks: II. Experimental results. Social Networks, 2(2), 119–141. https://doi.org/10.1016/0378-8733(79)90002-9
- Gan, X., Chang, R., & Wen, T. (2018). Overcoming barriers to off-site construction through engaging stakeholders: A two-mode social network analysis. Journal of Cleaner Production, 201, 735–747. https://doi.org/10.1016/j.jclepro.2018.07.299
- Glynn, C. J., & Huge, M. E. (2007). Opinions as norms. Communication Research, 345, 548–568. https://doi.org/10.1177/0093650207305236
- Golbeck, J., Grimes, J. M., & Rogers, A. (2010). Twitter use by the US Congress. Journal of the American Society for Information Science and Technology, 61, 1612–1621. https://doi.org/10.1002/asi.21344
- Guo, C. (2014). Regulation of fabrication and dissemination of rumors on the internet in the perspective of public figures. Chinese Journal of Law, 004, 158–174. https://doi.org/CNKI:SUN:LAWS.0.2014-04-010
- Hall, M. C. (2002). Travel safety, terrorism and the media: The significance of the issue-attention cycle. Current Issues in Tourism, 5(5), 458–466. https://doi.org/10.1080/13683500208667935
- Hanks, S. H. (1990). The organization life cycle: Integrating content and process. Journal of Small Business Strategy, 1, 1–12. http://doi.org/10.1057/978-1-137-47947-1
- Hargittai, E., & Litt, E. (2011). The tweet smell of celebrity success: Explaining variation in Twitter adoption among a diverse group of young adults. New Media & Society, 13(5), 824–842. https://doi.org/10.1177/1461444811405805
- Hongwei, S. (2012). The causes and Countermeasures of false Internet news in the "micro era". Decision Making Exploration, 12, 73–74. https://doi.org/CNKI:SUN:JCTY.0.2012-06-049
- Jiang, H., Qiang, M., & Lin, P. (2016). Assessment of online public opinions on large infrastructure projects: A case study of the three gorges project in China. Environmental Impact Assessment Review, 61, 38–51. https://doi.org/10.1016/j.eiar.2016.06.004
- Kelly, N. J., & Enns, P. K. (2010). Inequality and the dynamics of public opinion: The self-reinforcing link between economic inequality and mass preferences. American Journal of Political Science, 54(4), 855–870. https://doi.org/10.1111/j.1540-5907.2010.00472.x
- Li, N., Akin, H., Su, L. Y.-F., Brossard, D., Xenos, M., & Scheufele, D. A. (2016). Tweeting disaster: An analysis of online discourse about nuclear power in the wake of the Fukushima Daiichi nuclear accident. Journal of Science Communication, 15, A0201–A0220. https://doi.org/10.22323/2.15050202
- Liu, L., Liu, W., & Dong, W. (2017). Topic evolution of public opinion on weibo in earthquake events (pp. 241–247).
- Lu, P., & Zhang, N. (2016, November 11–13). Evaluation of risk of emergency network public opinion based on method of fuzzy grey evaluation. 2016 6th international conference on Mechatronics, Computer and Education Informationization (MCEI 2016), Atlantis Press.
- Ma, Y., Shu, X., Shen, S., Song, J., Li, G., & Liu, Q. (2014). Study on network public opinion dissemination and coping strategies in large fire disasters. Procedia Engineering, 71, 616–621. https://doi.org/10.1016/j.proeng.2014.04.088
- Morales, A. J., Borondo, J., Losada, J. C., & Benito, R. M. (2014). Efficiency of human activity on information spreading on Twitter. Social Networks, 39, 1–11. https://doi.org/10.1016/j.socnet.2014.03.007
- Nair, M. R., Ramya, G. R., & Sivakumar, P. B. (2017). Usage and analysis of Twitter during 2015 Chennai flood towards disaster management. Procedia Computer Science, 115, 350–358. http://doi.org/10.1016/j.procs.2017.09.089
- Nakov, P., Rosenthal, S., Kiritchenko, S., Mohammad, S. M., Kozareva, Z., Ritter, A., Stoyanov, V., & Zhu, X. (2016). Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts. Language Resources and Evaluation, 50(1), 35–65. https://doi.org/10.1007/s10579-015-9328-1
- Osborne, T., & Rose, N. (1999). Do the social sciences create phenomena?: The example of public opinion research. The British Journal of Sociology, 50(3), 367–396. https://doi.org/10.1111/j.1468-4446.1999.00367.x
- Otte, E., & Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28(6), 441–453. https://doi.org/10.1177/016555150202800601
- Popa, D., & Gavriliu, D. (2015). Gender representations and digital media. Procedia-Social and Behavioral Sciences, 180, 1199–1206. https://doi.org/10.1016/j.sbspro.2015.02.244
- Pourebrahim, N., Sultana, S., Edwards, J., Gochanour, A., & Mohanty, S. (2019). Understanding communication dynamics on Twitter during natural disasters: A case study of Hurricane Sandy. International Journal of Disaster Risk Reduction, 37, 101176. https://doi.org/10.1016/j.ijdrr.2019.101176
- Prell, C. (2012). Social network analysis: History, theory and methodology. Sage.
- Rizoiu, M.-A., Xie, L., Sanner, S., Cebrian, M., Yu, H., & Van Hentenryck, P. (2017, April 3-7). Expecting to be hip: Hawkes intensity processes for social media popularity. Proceedings of the 26th international conference on World Wide Web. International World Wide Web Conferences Steering Committee.
- Tan, X., Tu, Y., & Ma, Z. (2017). Analysis of the key users in accident public opinion spread on social network theory. Journal of the China Society for Scientific and Technical Information, 297–306.
- Wang, X., & Sun, R. (2017). Research about online public opinion spread during emergencies based on social network analysis—A case study of the Wei Zexi. Information Science, 16, 89–94. http://doi.org/CNKI:SUN:QBKX.0.2017-03-016
- Wen, Z., Yong, G., & Ronggui, H. (2019). Debate and evolution: reform and opening up as a network social trend of thought – taking 275 million microblogs from 2013 to 2018 as an analysis sample. Journalist, 1, 51–62. https://doi.org/CNKI:SUN:XWJZ.0.2019-01-005
- Wu, F., & Huberman, B. A. (2008). How public opinion forms. International Workshop on Internet and Network Economics. (pp. 334–341). Springer.
- Yang, J. (2012). Ways and means of using microblog to enhance the influence of mainstream media. Chinese Journalists, 3, 18–19. http://doi.org/CNKI:SUN:ZGJZ.0.2012-03-013.
- Yuan, J., Chen, K., Li, W., Ji, C., Wang, Z., & Skibniewski, M. J. (2018). Social network analysis for social risks of construction projects in high-density urban areas in China. Journal of Cleaner Production, 198, 940–961. https://doi.org/10.1016/j.jclepro.2018.07.109
- Zhan, Y., Liu, R., Li, Q., Leischow, S. J., & Zeng, D. D. (2017). Identifying topics for e-cigarette user-generated contents: A case study from multiple social media platforms. Journal of Medical Internet Research, 19(1), e24. https://doi.org/10.2196/jmir.5780
- Zhang, H., & Jiang, S. (2012). Comparison of the relative credibility of microblog and traditional media based on the survey data of newspaper readers in Chengdu. News Research Guide, 3, 52–54.
- Zhang, C., & Yu, H. (2011). Female expression in microblog space: opportunities, problems and prospects (pp. 106–110).
- Zixiong, L., & Zhuying, W. (2011). Trusted fake news – a psychological discussion on the audience's acceptance of false information. Modern communication (Journal of China Media University), 7, 56–59. https://doi.org/CNKI:SUN:XDCB.0.2011-07-022