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All issues Volume 119 (2021) SHS Web Conf., 119 (2021) 07003 References
Open Access
Issue
SHS Web Conf.
Volume 119, 2021
3rd International Conference on Quantitative and Qualitative Methods for Social Sciences (QQR’21)
Article Number 07003
Number of page(s) 9
Section Technology and Society / Covid-19 Innovations
DOI https://doi.org/10.1051/shsconf/202111907003
Published online 24 August 2021
  1. H. Wang, Z. Wang, Y. Dong et al., Phase-adjusted estimation of the number of coronavirus disease 2019 cases in Wuhan, China, Cell Discovery, vol. 6 (2020). [Google Scholar]
  2. W. H. Organization, Coronavirus, https://www.who. int/health-topics/coronavirus#tab=tab_1 (2020). [Google Scholar]
  3. Michal Kosinski, David Stillwell, and Thore Graepel, Private traits and attributes are predictable from digital records of human behavior, Proceeding of the National Academy of Sciences of The United States of America, 110 (2013). [Google Scholar]
  4. Kahr, M., Leitner, M., Ruthmair, M., Sinnl, M. Benders decomposition for competitive influence maximization in (social) networks. Omega, 102264. doi: 10.1016/j.omega.2020.102264 (2020). [Google Scholar]
  5. Bazzaz Abkenar, S., Haghi Kashani, M., Mahdipour, E., & Mahdi Jameii, S. Big data analytics meets social media: A systematic review of techniques, open issues, and future directions. Telematics and Informatics, 101517. doi: 10.1016/j.tele.2020.101517 (2020). [Google Scholar]
  6. Y. Feng, P. Zhou, D. Wu, and Y. Hu, Accurate Content Push for Content-Centric Social Networks: A Big Data Support Online Learning Approach, IEEE Transactions on Emerging Topics in Computational Intelligence, no. 99 (2018). [Google Scholar]
  7. J. Heidemann, M. Klier, and F. Probst, Online social networks: A survey of a global phenomenon, Computer networks, vol. 56 (2012). [Google Scholar]
  8. Mohamed Chiny, Omar Bencharef, Moulay Youssef Hadi, Younes Chihab, A ClientCentric Evaluation System to Evaluate Guest’s Satisfaction on Airbnb Using Machine Learning and NLP, Applied Computational Intelligence and Soft Computing (2021). [Google Scholar]
  9. Zhang, X., Saleh, H., Younis, E. M. G., Sahal, R., & Ali, A.A. Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System. Complexity, 2020, 1–10. doi: 10.1155/2020/6688912 (2020) [Google Scholar]
  10. Parul Pandey, Simplifying Sentiment Analysis using VADER in Python (on Social Media Text), https://medium.com/analytics-vidhya/simplifying-social-media-sentiment-analysis-using-vader-in-python-f9e6ec6fc52f (2018). [Google Scholar]
  11. C.J. Hutto, Eric Gilbert, VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text, Conference: Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media At: Ann Arbor, MI (2015). [Google Scholar]
  12. World Health Organization, Archived: WHO Timeline - COVID-19, https://www.who.int/news/item/27-04-2020-who-timeline—covid-19, (April 2020). [Google Scholar]
  13. World Bank, World Development Indicators, https://datatopics.worldbank.org/world-development-indicators/ (2018). [Google Scholar]
  14. Pennebaker, J. W., Francis, M., & Booth, R. Linguistic Inquiry and Word Count: LIWC 2001. Mahwah, NJ: Erlbaum (2001). [Google Scholar]
  15. Pennebaker, J. W., Chung, C. K., Ireland, M., Gonzales, A., & Booth, R.J. The development and psychometric properties of LIWC2007. Austin, TX: LIWC net (2007). [Google Scholar]
  16. Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D.M. General Inquirer. Cambridge, MA: MIT Press (1966). [Google Scholar]
  17. Hu, M., & Liu, B. Mining and summarizing customer reviews. In Proc. SIGKDD KDM-04 (2004). [Google Scholar]
  18. Bradley, M. M., & Lang, P.J. Affective norms for English words (ANEW): Instruction manual and affective ratings (1999). [Google Scholar]
  19. Le Monde avec Reuters, Twitter supprime 170 000 comptes diffusant des messages favorables à la Chine, https://www.lemonde.fr/pixels/article/2020/06/12/twitter-supprime-170-000-comptes-diffusant-des-messages-favorables-a-la-chine_6042620_4408996.html (June 2020). [Google Scholar]
  20. Shengqi Wu, Huaizhen Kou, Chao Lv, Wanli Huang, Lianyong Qi, Hao Wang, Service Recommendation with High Accuracy and Diversity, Wireless Communications and Mobile Computing (2020). [Google Scholar]
  21. World Bank, COVID-19 to Plunge Global Economy into Worst Recession since World War II, https://www.worldbank.org/en/news/press-release/2020/06/08/covid-19-to-plunge-global-economy-into-worst-recession-since-world-war-ii (June 2020). [Google Scholar]
  22. Kamaran H. Manguri, Rebaz N. Ramadhan, Pshko R. Mohammed Amin, Twitter Sentiment Analysis on Worldwide COVID-19 Outbreaks, Kurdistan Journal of Applied Research (May 2020). [Google Scholar]
  23. Toni Pano, Asha Kashef, A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19, Big Data and Cognitive Computing, Vol. 4(4) (2020). [Google Scholar]
  24. Valdez D., ten Thij M., Bathina K., Rutter L.A., Bollen J., Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data, J Med Internet Res, Vol 22(12) (2020). [Google Scholar]

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