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- Jun 2021
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Antico, L., & Corradi-Dell’Acqua, C. (2021). Far from the eyes, far from the heart. COVID-19 confinement dampened sensitivity to painful facial features. [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/ewvp7
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- Feb 2021
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Ye, Y., Zhang, Q., Ruan, Z., Cao, Z., Xuan, Q., & Zeng, D. D. (2020). Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission. Physical Review E, 102(4), 042314. https://doi.org/10.1103/PhysRevE.102.042314
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Vigfusson, Y., Karlsson, T. A., Onken, D., Song, C., Einarsson, A. F., Kishore, N., Mitchell, R. M., Brooks-Pollock, E., Sigmundsdottir, G., & Danon, L. (2021). Cell-phone traces reveal infection-associated behavioral change. Proceedings of the National Academy of Sciences, 118(6). https://doi.org/10.1073/pnas.2005241118
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- Jan 2021
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Li, J., & Zheng, H. (2020). Online InformationSeeking and Disease Prevention Intent During COVID-19 Outbreak. Journalism & Mass Communication Quarterly, 1077699020961518. https://doi.org/10.1177/1077699020961518
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Gratton, C., Gagnon-St-Pierre, É., & Markovits, H. (2020). When forewarned is not forearmed: The paradoxical effect of single warnings attached to repeated fake news [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/h5cxp
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Beytía, P., & Infante, C. C. (2020). Digital Pathways, Pandemic Trajectories. Using Google Trends to Track Social Responses to COVID-19 [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/yndb7
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- Apr 2020
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psyarxiv.com psyarxiv.com
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Olapegba, P. O., Ayandele, O., Kolawole, S. O., Oguntayo, R., Gandi, J. C., Dangiwa, A. L., … Iorfa, S. K. (2020, April 12). COVID-19 Knowledge and Perceptions in Nigeria. https://doi.org/10.31234/osf.io/j356x
Tags
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arxiv.org arxiv.org
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Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19. ArXiv:2004.04544 [Physics, q-Bio]. http://arxiv.org/abs/2004.04544
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arxiv.org arxiv.org
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Nanni, M., Andrienko, G., Boldrini, C., Bonchi, F., Cattuto, C., Chiaromonte, F., Comandé, G., Conti, M., Coté, M., Dignum, F., Dignum, V., Domingo-Ferrer, J., Giannotti, F., Guidotti, R., Helbing, D., Kertesz, J., Lehmann, S., Lepri, B., Lukowicz, P., … Vespignani, A. (2020). Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. ArXiv:2004.05222 [Cs]. http://arxiv.org/abs/2004.05222
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featuredcontent.psychonomic.org featuredcontent.psychonomic.org
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Psychonomic Society. Reduce Face Touching. Psychonomic.org. https://featuredcontent.psychonomic.org/behavioral-science-recommendations/reduce-face-touching/
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Punn, N. S., Sonbhadra, S. K., & Agarwal, S. (2020). COVID-19 Epidemic Analysis using Machine Learning and Deep Learning Algorithms [Preprint]. Health Informatics. https://doi.org/10.1101/2020.04.08.20057679
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cdn2.hubspot.net cdn2.hubspot.net
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Savanta Coronavirus Data Tracker
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psyarxiv.com psyarxiv.com
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Sailer, M., Stadler, M., Botes, E., Fischer, F., & Greiff, S. (2020, April 9). Science knowledge and trust in medicine affect individuals’ behavior in pandemic crises. https://doi.org/10.31234/osf.io/tmu8f
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www.nsf.gov www.nsf.gov
- Jul 2018
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wendynorris.com wendynorris.com
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The overload of information, for example, is becoming so extensive that taking advantage of only the tiniest fraction of it not only blows apart the principle of instantaneity and 'real-time' communication, but also slows down operators to a pomt where they lose themselves in the eternity of electronically networked information.
High tempo Information overload exacerbates time compression and thus impacts temporal sensemaking through typical means via chronologies, linear information processing, and past/present/future contexts.
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