Mulukom, V. van, Pummerer, L., Alper, S., Bai, (Max) Hui, Cavojova, V., Farias, J. E. M., Kay, C. S., Lazarevic, L., Lobato, E. J. C., Marinthe, G., Banai, I. P., Šrol, J., & Zezelj, I. (2020). Antecedents and consequences of COVID-19 conspiracy theories: A rapid review of the evidence. PsyArXiv. https://doi.org/10.31234/osf.io/u8yah

COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved October 10, 2020, from https://covid-19.iza.org/publications/dp13574/

Burel, Gregoire; Farrell, Tracie; Mensio, Martino; Khare, Prashant and Alani, Harith (2020). Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic. In: Proceedings of the 12th International Social Informatics Conference (SocInfo), LNCS.

Gallagher, R. J., Doroshenko, L., Shugars, S., Lazer, D., & Welles, B. F. (2020). Sustained Online Amplification of COVID-19 Elites in the United States. ArXiv:2009.07255 [Physics]. http://arxiv.org/abs/2009.07255

Thread, C. (2020, June 19). Trolls and Tribulations: Social Media and Public Health. Medium. https://medium.com/@gocommonthread/trolls-and-tribulations-social-media-and-public-health-499bf5c8727c

Hu, Y., & Wang, R.-Q. (2020). Understanding the removal of precise geotagging in tweets. Nature Human Behaviour, 1–3. https://doi.org/10.1038/s41562-020-00949-x

Hoogeveen, S., Sarafoglou, A., & Wagenmakers, E.-J. (2020). Laypeople Can Predict Which Social-Science Studies Will Be Replicated Successfully: Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920919667

Miyoshi, S., Jusup, M., & Holme, P. (2020). Flexible imitation mechanisms suppress epidemics through better vaccination. ArXiv:2009.00443 [Physics, q-Bio]. http://arxiv.org/abs/2009.00443

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

Dynamics of Social Mobility during the COVID-19 Pandemic in Canada. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 4, 2020, from https://covid-19.iza.org/publications/dp13376/

Moya, C., Cruz y Celis Peniche, P. D., Kline, M. A., & Smaldino, P. (2020). Dynamics of Behavior Change in the COVID World [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/kxajh

Research, B. and S. S. at N. (2020, May 25). Imagining a different online world. Behavioural and Social Sciences at Nature Research. http://socialsciences.nature.com/users/397838-philipp-lorenz-spreen/posts/imagining-a-different-online-world

Washington, D. of C. 1100 C. A. N. S. 1300B, & Dc 20036. (n.d.). PolitiFact - Fact-checking ‘Plandemic 2’: Another video full of conspiracy theories about COVID-19. @politifact. Retrieved August 27, 2020, from https://www.politifact.com/article/2020/aug/18/fact-checking-plandemic-2-video-recycles-inaccurat/

https://twitter.com/BrentWRoberts/status/1297617960916471813

Velásquez-Rojas, F., Ventura, P. C., Connaughton, C., Moreno, Y., Rodrigues, F. A., & Vazquez, F. (2020). Disease and information spreading at different speeds in multiplex networks. Physical Review E, 102(2), 022312. https://doi.org/10.1103/PhysRevE.102.022312

Khanam, K. Z., Srivastava, G., & Mago, V. (2020). The Homophily Principle in Social Network Analysis. ArXiv:2008.10383 [Physics]. http://arxiv.org/abs/2008.10383

Webinars in English 🇺🇸—YouTube. (n.d.). Retrieved 24 August 2020, from https://www.youtube.com/playlist?list=PL0n8am2uBRCBUZI5ftm2jQ63tlYrtY4_I

Engelhardt, R., Hendricks, V. F., & Stærk-Østergaard, J. (2020). The Wisdom and Persuadability of Threads. ArXiv:2008.05203 [Physics]. http://arxiv.org/abs/2008.05203

Paris, Marseille named as high-risk COVID zones, making curbs likelier. (2020, August 14). Reuters. https://uk.reuters.com/article/uk-health-coronavirus-france-idUKKCN25A0LC

Barrios, J. M., & Hochberg, Y. (2020). Risk Perception Through the Lens of Politics in the Time of the COVID-19 Pandemic (Working Paper No. 27008; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27008

Tasnim, S., Hossain, M. M., & Mazumder, H. (2020). Impact of rumors or misinformation on coronavirus disease (COVID-19) in social media [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/uf3zn

Dimant, E., van Kleef, G. A., & Shalvi, S. (2020). Requiem for a Nudge: Framing effects in nudging honesty. Journal of Economic Behavior & Organization, 172, 247–266. https://doi.org/10.1016/j.jebo.2020.02.015

Haman, M. (2020). The use of Twitter by state leaders and its impact on the public during the COVID-19 pandemic. https://doi.org/10.31235/osf.io/u4maf

JAMA Network - Discussing preprint servers and social media.

Sakakibara, R., & Ozono, H. (2020). Psychological Research on the COVID-19 Crisis in Japan: Focusing on Infection Preventive Behaviors, Future Prospects, and Information Dissemination Behaviors. https://doi.org/10.31234/osf.io/97zye

Leppanen, J., Tosunlar, L., Blackburn, R., Williams, S., Tchanturia, K., & Sedgewick, F. (2020, July 6). Critical incidents in anorexia nervosa.

Neill, R., Blair, C., Best, P., McGlinchey, E., & Armour, C. (2020). Media Consumption and Mental Health during COVID-19 lockdown: A UK Cross-sectional study across England, Wales, Scotland and Northern [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/5d9fc

Yang, G., Csikász-Nagy, A., Waites, W., Xiao, G., & Cavaliere, M. (2020). Information Cascades and the Collapse of Cooperation. Scientific Reports, 10(1), 8004. https://doi.org/10.1038/s41598-020-64800-z

Kitamura, S., & Yamada, K. (2020). Social Comparisons and Cooperation During COVID-19 [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/rsbmz

Branas-Garza, P., Jorrat, D. A., Alfonso, A., Espin, A. M., García, T., & Kovarik, J. (2020). Exposure to the Covid-19 pandemic and generosity in southern Spain [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/6ktuz

Paakkari, L., & Okan, O. (2020). COVID-19: Health literacy is an underestimated problem. The Lancet Public Health, S2468266720300864. https://doi.org/10.1016/S2468-2667(20)30086-4

Tips on Using Science Twitter During COVID-19. (2020, April 15). PLOS SciComm. https://scicomm.plos.org/2020/04/15/tips-on-using-science-twitter-during-covid-19/

Plata, C. A., Pigani, E., Azaele, S., Callejas, V., Palazzi, M. J., Solé-Ribalta, A., Meloni, S., & Suweis, J. B.-H. S. (2020). Neutral Theory for competing attention in social networks. ArXiv:2006.07586 [Physics]. http://arxiv.org/abs/2006.07586

Guerrero, R. J. A., Parra, A. B., Cahua, J. C. H., & Banaszak-Holl, J. (2020). Psychological problems associated with coping strategies: A web-based study in Peruvian population during the COVID-19 pandemic. https://doi.org/10.31234/osf.io/uarwz

Coronavirus Disease (COVID-19): Psychological, Behavioral, Interpersonal Effects, and Clinical Implications for Health Systems | Frontiers Research Topic. (n.d.). Retrieved June 11, 2020, from https://www.frontiersin.org/research-topics/13561/coronavirus-disease-covid-19-psychological-behavioral-interpersonal-effects-and-clinical-implication

British Journal of Social Psychology. (n.d.). Wiley Online Library. https://doi.org/10.1111/(ISSN)2044-8309

where's the line though? every output generates a feedback loop with the hivemind, turning into input to ourselves with our cracking, overwhelmed, filters

it's unrealistic to wish everyone to see jokes are jokes, to rely on journalists to generate unbiased facts, and politicians as self serving leeches, err that's my bias speaking

Cinus, F., Bonchi, F., Monti, C., & Panisson, A. (2020). Generating Realistic Interest-Driven Information Cascades. Proceedings of the International AAAI Conference on Web and Social Media, 14, 107–118.

Jazayeri, A., & Yang, C. C. (2020). Motif Discovery Algorithms in Static and Temporal Networks: A Survey. ArXiv:2005.09721 [Physics]. http://arxiv.org/abs/2005.09721

Costantini, S., De Meo, P., Giorgianni, A., Migliorato, V., Provetti, A., & Salvia, F. (2020). Exploring Low-degree Nodes First Accelerates Network Exploration. ArXiv:2005.08050 [Physics]. http://arxiv.org/abs/2005.08050

Makhortykh, M., Urman, A., & Ulloa, R. (2020). How search engines disseminate information about COVID-19 and why they should do better. Harvard Kennedy School Misinformation Review, 1(COVID-19 and Misinformation). https://doi.org/10.37016/mr-2020-017

Pastor-Escuredo, D., & Tarazona, C. (2020). Characterizing information leaders in Twitter during COVID-19 crisis. ArXiv:2005.07266 [Physics]. http://arxiv.org/abs/2005.07266

Bode, L., & Vraga, E. (2020 May 7). Analysis | Americans are fighting coronavirus misinformation on social media. Washington Post.https://www.washingtonpost.com/politics/2020/05/07/americans-are-fighting-coronavirus-misinformation-social-media/

de Montjoye, Y. et al. (2020 April 02). Evaluating COVID-19 contact tracing apps? Here are 8 privacy questions we think you should ask. Computational Privacy Group. https://cpg.doc.ic.ac.uk/blog/evaluating-contact-tracing-apps-here-are-8-privacy-questions-we-think-you-should-ask/

Fränken, J.-P., & Pilditch, T. (2020). Cascades across networks are sufficient for the formation of echo chambers: An agent-based model. https://doi.org/10.31234/osf.io/8rgkc

Ciranka, S. K., & van den Bos, W. (2020). A Bayesian Model of Social Influence under Risk and Uncertainty [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/mujek

Brangwynne, C. (2020 April 29). How a Landmark Physics Paper from the 1970s Uncannily Describes the COVID-19 Pandemic. Scientific American Blog Network. https://blogs.scientificamerican.com/observations/how-a-landmark-physics-paper-from-the-1970s-uncannily-describes-the-covid-19-pandemic/

Hope, T., Borchardt, J., Portenoy, J., Vasan, K., & West, J. (2020, May 6). Exploring the COVID-19 network of scientific research with SciSight. Medium. https://medium.com/ai2-blog/exploring-the-covid-19-network-of-scientific-research-with-scisight-f75373320a8c

PandemicPolitics. Pandemic politics: Political attitudes and crisis communication. https://www.pandemicpolitics.net

Espinosa, F. d., Metko, A., Raimondi, M., Impenna, M., & Scognamiglio, E. (2020, April 10). A Model of Support for Families of Children with Autism Living in the COVID-19 Lockdown: Lessons from Italy. https://doi.org/10.31234/osf.io/48cme

Ferres, L. (2020 April 10). COVID19 mobility reports. Leo's Blog. https://leoferres.info/blog/2020/04/10/covid19-mobility-reports/

Serrano, J. C. M., Papakyriakopoulos, O., & Hegelich, S. (2020). Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok. ArXiv:2004.05478 [Cs]. http://arxiv.org/abs/2004.05478

Psychonomic Society (2020 April 29). Keep Your Social Distance Up (Tips from behavioral scientists to help slow the spread of COVID-19). https://www.youtube.com/watch?v=ZVL66099O0s

Ammerman, B. A., Burke, T. A., Jacobucci, R., & McClure, K. (2020, April 6). Preliminary Investigation of the Association Between COVID-19 and Suicidal Thoughts and Behaviors in the U.S. https://doi.org/10.31234/osf.io/68djp

Resource List for pandemic and COVID-19 related topics.

Santana, C., Botta, F., Barbosa, H., Privitera, F., Menezes, R., Di Clemente, R. (2020 April 8). COVID-19 UK mobility report. Covid19-uk-mobility.github.io. https://covid19-uk-mobility.github.io/First-report.html

Koren, M., & Petö, R. (2020, March 20). Business disruptions from social distancing. Cornel University. arXiv:2003.13983.

Kamel Boulos, M.N., Geraghty, E.M. Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr 19, 8 (2020). https://doi.org/10.1186/s12942-020-00202-8

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

Michael Veale on Twitter.

British Psychological Society. Responding to Coronavirus: Resources and support. https://www.bps.org.uk/responding-coronavirus

Nielsen, R.K., Fletcher, R., Newman, N., Brennen, S., Howard, P.N. (2020 April 15). Navigating the ‘infodemic’: how people in six countries access and rate news and information about coronavirus. Reuters Institute. https://reutersinstitute.politics.ox.ac.uk/infodemic-how-people-six-countries-access-and-rate-news-and-information-about-coronavirus

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

Alam, F., Sajjad, H., Imran, M., & Ofli, F. (2020). Standardizing and Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing. ArXiv:2004.06774 [Cs]. http://arxiv.org/abs/2004.06774

Seems like this is a critical assumption to examine for current media literacy/misinformation discussions. As networks become very large and very flat, does this assumption of reciprocity and good faith hold? (I'm thinking, here, of people whose expertise I trust in one domain but perhaps not in another, or the fact that sometimes I'm talking to one part of my network and not really "actively seeking information" for other parts.)

The Information is Aid perspective that drives my research agenda.