Banerjee, D., Meena, K.S.: COVID-19 as an “Infodemic” in public health: Critical role of the social media. Front. Public Health 9, 231–238 (2021)
Barbieri, F., Camacho-Collados, J., Espinosa Anke, L., Neves, L.: TweetEval: unified benchmark and comparative evaluation for tweet classification. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1644–1650. Association for Computational Linguistics (2020)
Bradley, M.M., Lang, P.J.: Affective norms for English words (ANEW): Instruction manual and affective ratings. Tech. rep., the Centre for Research in Psychophysi-ology, University of Florida (1999)
Chen, E., Lerman, K., Ferrara, E.: Tracking social media discourse about the COVID-19 pandemic: development of a public coronavirus Twitter data set. JMIR Public Health Surveill. 6(2), e19273 (2020)
Chen, N., Chen, X., Zhong, Z., Pang, J.: From #jobsearch to #mask: improving COVID-19 cascade prediction with spillover effects. In: Proceedings of 2021 International Conference on Advances in Social Networks Analysis and Min-ing(ASONAM), pp. 455–462. ACM (2021)
Cinelli, M., et al.: The COVID-19 social media infodemic. Sci. Rep. 10(1), 1–10 (2020)
Diener, E., Emmons, R.A., Larsen, R.J., Griffin, S.: The satisfaction with life scale. J. Pers. Assess. 49(1), 71–75 (1985)
Diener, E., Oishi, S., Lucas, R.E.: Personality, culture, and subjective well-being: Emotional and cognitive evaluations of life. Annu. Rev. Psychol. 54(1), 403–425 (2003)
Dubey, S., et al.: Psychosocial impact of COVID-19. Diab. Metabol. Synd. Clin. Res. Rev. 14(5), 779–788 (2020)
Duong, V., Luo, J., Pham, P., Yang, T., Wang, Y.: The ivory tower lost: how college students respond differently than the general public to the COVID-19 pandemic. In: Proceedings 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 126–130. IEEE (2020)
Engel de Abreu, P.M., Neumann, S., Wealer, C., Abreu, N., Coutinho Macedo, E., Kirsch, C.: Subjective well-being of adolescents in Luxembourg, Germany, and Brazil during the COVID-19 pandemic. J. Adolesc. Health 69(2), 211–218 (2021)
Fernando, S., López, J.A.D., Şerban, O., Gómez-Romero, J., Molina-Solana, M., Guo, Y.: Towards a large-scale Twitter observatory for political events. Futur. Gener. Comput. Syst. 110, 976–983 (2020)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)
Guarino, S., Pierri, F., Giovanni, M.D., Celestini, A.: Information disorders during the COVID-19 infodemic: the case of Italian Facebook. Online Soc. Netw. Media 22, 100124 (2021)
Hernandez, R.G., Hagen, L., Walker, K., O’Leary, H., Lengacher, C.: The COVID-19 vaccine social media infodemic: healthcare providers’ missed dose in addressing misinformation and vaccine hesitancy. Hum. Vacc. Immunother. 17(9), 2962–2964 (2021)
Hu, Z., Lin, X., Kaminga, A.C., Xu, H.: Impact of the COVID-19 epidemic on lifestyle behaviors and their association with subjective well-being among the general population in mainland China: Cross-sectional study. J. Med. Internet Res. 22(8), e21176 (2020)
Huang, S., Lv, T., Zhang, X., Yang, Y., Zheng, W., Wen, C.: Identifying node role in social network based on multiple indicators. PLoS ONE 9(8), e103733 (2014)
Jaidka, K., Giorgi, S., Schwartz, H.A., Kern, M.L., Ungar, L.H., Eichstaedt, J.C.: Estimating geographic subjective well-being from Twitter: a comparison of dictionary and data-driven language methods. Proc. Natl. Acad. Sci. 117(19), 10165– 10171 (2020)
Kupavskii, A., et al.: Prediction of retweet cascade size over time. In: Proc. 2012 International Conference on Information and Knowledge Management (CIKM), pp. 2335–2338. ACM (2012)
Li, Y.M., Lai, C.Y., Chen, C.W.: Discovering influencers for marketing in the blogosphere. Inf. Sci. 181(23), 5143–5157 (2011)
Liu, Y., et al.: RoBERTa: A robustly optimized BERT pretraining approach. In: ICLR (2019)
Mirbabaie, M., Bunker, D., Stieglitz, S., Marx, J., Ehnis, C.: Social media in times of crisis: learning from Hurricane Harvey for the coronavirus disease 2019 pandemic response. J. Inf. Technol. 35(3), 195–213 (2020)
Ou, X., Li, H.: Ynu oxz @ haspeede 2 and AMI: XLM-RoBERTa with ordered neurons LSTM for classification task at EVALITA 2020. In: Proceedings of 2020 Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA), vol. 2765 (2020)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web. Tech. rep, Stanford InfoLab (1999)
Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: Sentiment analysis in Twitter. In: Proceedings of 2017 International Workshop on Semantic Evaluation (SemEval), pp. 502–518 (2017)
Struweg, I.: A twitter social network analysis: the South African health insurance bill case. In: Hattingh, M., Matthee, M., Smuts, H., Pappas, I., Dwivedi, Y.K., Mäntymäki, M. (eds.) I3E 2020. LNCS, vol. 12067, pp. 120–132. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45002-1 11
Wang, Y., Shen, H., Liu, S., Gao, J., Cheng, X.: Cascade dynamics modeling with attention-based recurrent neural network. In: Proceedings of 2017 International Joint Conference on Artificial Intelligence (IJCAI), pp. 2985–2991. IJCAI (2017)
Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitters. In: Proceedings of 2010 ACM International Conference on Web Search and Data Mining (WSDM), pp. 261–270 (2010)
Wilkinson, M.D., et al.: The fair guiding principles for scientific data management and stewardship. Sci. Data 3(1), 1–9 (2016)
Yang, C., Srinivasan, P.: Life satisfaction and the pursuit of happiness on Twitter. PLoS ONE 11(3), e0150881 (2016)
Zarocostas, J.: How to fight an infodemic. Lancet 395(10225), 676 (2020)
Zhou, X., Jin, S., Zafarani, R.: Sentiment paradoxes in social networks: why your friends are more positive than you? In: Proceedings of 2020 International Conference on Web and Social Media (ICWSM), pp. 798–807. AAAI Press (2020)
