Open Access
Issue
E3S Web Conf.
Volume 253, 2021
2021 International Conference on Environmental and Engineering Management (EEM 2021)
Article Number 02010
Number of page(s) 5
Section Big Data Environment Management Application and Industry Research
DOI https://doi.org/10.1051/e3sconf/202125302010
Published online 06 May 2021
  1. The New Power of Political Influence, CES (Center for European Studies) and Suomen Toivo Think Tank [Google Scholar]
  2. A Chinese microblogging website that was launched by Sina Corporation. It’s one of the biggest social media platforms in China. See: https://en.m.wikipedia.org/wiki/Sina_Weibo [Google Scholar]
  3. Constance Duncombe. “The Politics of Twitter: Emotions and the Power of Social Media.” International Political Sociology (2019) 13, 409–429 [Google Scholar]
  4. Zappavigna, M., 2015. Searchable talk: the linguistic function of hashtags. Soc. Semiot. 25 (3), 274–291. [Google Scholar]
  5. Scott, K., 2015. The pragmatics of hashtags: inference and conversational style on Twitter. J. Pragmat. 81, 8–20. [Google Scholar]
  6. Kate Scott, 2017. “Hashtags work everywhere”: The pragmatic functions of spoken hashtags. [Google Scholar]
  7. An American photo and video-sharing social networking service owned by Facebook, Inc. See: https://en.m.wikipedia.org/wiki/Instagram [Google Scholar]
  8. A video-sharing social networking service owned by ByteDance, a Chinese company founded in 2012 by Zhang Yiming. See: https://en.m.wikipedia.org/wiki/TikTok [Google Scholar]
  9. Jinpeng Chen, Yu Liu, Ming Zou, 2017. “User emotion for modeling retweeting behaviors.” Neural Networks 96(2017)11–21 [Google Scholar]
  10. Yen-Liang Chen, Chia-Ling Chang, Chin-Sheng Yeh, 2017. “Emotion classification of YouTube videos” Decision Support Systems 101(2017) 40–50 [Google Scholar]
  11. Boulton M. (2009) Designing for the Web. Penarth: Mark Boulton Design Ltd Silveira LM (2011) Introduction to color theory (in Portuguese). Ed. UTFPR [Google Scholar]
  12. Krishnan Vasudevan. Design of Communication: Two Contexts for Understanding How Design Shapes Digital Media. Journalism & Mass Communication Quarterly 1–16. © 2020 AEJMC, cited in Eyal, N. (2014). Hooked: How to build habit-forming products. Penguin. [Google Scholar]
  13. Yen-Liang Chen, Chia-Ling Chang, Chin-Sheng Yeh, 2017. “Emotion classification of YouTube videos” Decision Support Systems 101(2017) 40–50 [Google Scholar]
  14. Renata G. Bianchi & Vania P. A. Neris & Anderson L. Ara, Tags vs. observers - a study on emotions tagged and emotions felt with Flickr pictures, Multimedia Tools and Applications. Berlin: Springer Science+Business Media, LLC, 2019. [Google Scholar]
  15. Debashis Naskar, Sanasam Ranbir Singh, Durgesh Kumar, Sukumar Nandi, and Eva Onaindia de la Rivaherrera. 2020. Emotion Dynamics of Public Opinions on Twitter. ACM Trans. Inf. Syst. 38, 2, Article 18 (March 2020), 24 pages. [Google Scholar]
  16. X. Sun, et al., Detecting users’ anomalous emotion using social media for business intelligence, J. Comput. Sci. (2017), http://dx.doi.org/10.1016/j.jocs.2017.05.029 [Google Scholar]
  17. Cherniece J. Plume & Emma L. Slade. Sharing of Sponsored Advertisements on Social Media: A Uses and Gratifications Perspective. The Author(s) 2018, cited in Boland, H. (2017). Facebook revenue soars as mobile advertising sales boom. [Google Scholar]
  18. Karolien Poels &Siegfried Dewitte. Getting a Line on Print Ads: Pleasure and Arousal Reactions Reveal an Implicit Advertising Mechanism. Pages 63–74 | Published online: 04 Mar 2013 [Google Scholar]
  19. Virda Setyani, Yu-Qian Zhu, Achmad Nizar Hidayanto, Puspa Indahati Sandhyaduhita, Bo Hsiao. Exploring the psychological mechanisms from personalized advertisements to urge to buy impulsively on social media. International Journal of Information Management. Volume 48, October 2019, Pages 96–107 [Google Scholar]
  20. https://www.sohu.com/a7318924804_120050174 [Google Scholar]

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