Horita, Y., & Yamazaki, M. (2022). Generalized and behavioral trust: Correlation with nominating close friends in a social network. PsyArXiv. https://doi.org/10.31234/osf.io/xu8k3

I love that Johannes Klingbiel highlights having his own place on the Internet as a means to learn. While I suspect that part of the idea here is to learn about the web and programming, it's also important to have a place you can more easily look over and review as well as build out on as one learns. This dovetails in part with his third reason to have his own website: "to build". It's much harder to build out a learning space on platforms like Medium and Twitter. It's not as easy to revisit those articles and notes as those platforms aren't custom built for those sorts of learning affordances.

Building your own website for learning makes it by definition a learning management system. The difference between my idea of a learning management system here and the more corporate LMSes (Canvas, Blackboard, Moodle, etc.) is that you can change and modify the playground as you go. While your own personal LMS may also be a container for holding knowledge, it is a container for building and expanding knowledge. Corporate LMSes aren't good at these last two things, but are built toward making it easier for a course facilitator to grade material.

We definitely need more small personal learning management systems. (pLMS, anyone? I like the idea of the small "p" to highlight the value of these being small.) Even better if they have social components like some of the IndieWeb building blocks that make it easier for one to build a personal learning network and interact with others' LMSes on the web. I see some of this happening in the Digital Gardens space and with people learning and sharing in public.

[[Flancian]]'s Anagora.org is a good example of this type of public learning space that is taking the individual efforts of public learners and active thinkers and knitting their efforts together to facilitate a whole that is bigger than the sum of it's pieces.

Vega-Oliveros, D. A., Grande, H. L. C., Iannelli, F., & Vazquez, F. (2021). Bi-layer voter model: Modeling intolerant/tolerant positions and bots in opinion dynamics. The European Physical Journal Special Topics, 230(14–15), 2875–2886. https://doi.org/10.1140/epjs/s11734-021-00151-8

Edge Computing: What It Is and Why It Matters0 https://en.itpedia.nl/2021/12/29/edge-computing-what-it-is-and-why-it-matters/ Edge computing is an emerging new trend in cloud data storage that improves how we access and process data online. Businesses dealing with high-frequency transactions like banks, social media companies, and online gaming operators may benefit from edge computing.

Kan, U., Feng, M., & Porter, M. A. (2021). An Adaptive Bounded-Confidence Model of Opinion Dynamics on Networks. ArXiv:2112.05856 [Physics]. http://arxiv.org/abs/2112.05856

Kumar, A., Chowdhary, S., Capraro, V., & Perc, M. (2021). Evolution of honesty in higher-order social networks. Physical Review E, 104(5), 054308. https://doi.org/10.1103/PhysRevE.104.054308

Kwiek, M. (2021). The Globalization of Science: The Increasing Power of Individual Scientists. MetaArXiv. https://doi.org/10.31222/osf.io/gj4aq

Mazumdar, S., & Thakker, D. (2020). Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks. Future Internet, 12(12), 210. https://doi.org/10.3390/fi12120210

https://www.mdpi.com/1999-5903/12/12/210

Mattei, M., Caldarelli, G., Squartini, T., & Saracco, F. (2021). Italian Twitter semantic network during the Covid-19 epidemic. EPJ Data Science, 10(1), 1–27. https://doi.org/10.1140/epjds/s13688-021-00301-x

Manshour, P., & Montakhab, A. (2021). Dynamics of social balance on networks: The emergence of multipolar societies. Physical Review E, 104(3), 034303. https://doi.org/10.1103/PhysRevE.104.034303

Marley, J., Blanche, M., Bulut, A., Bamber, L., McVay, S., Adeyanju, A., & Worsfold, S. (2021). The Digital Resilience Network [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/m8dbc

Taylor, L. (2021). The Venezuelan health-care workers secretly collecting COVID stats. Nature, 597(7874), 20–21. https://doi.org/10.1038/d41586-021-02276-1

Liu, Q., & Chai, L. (2021). Opinion Dynamics Models with Memory in Coopetitive Social Networks: Analysis, Application and Simulation. ArXiv:2108.03234 [Physics]. http://arxiv.org/abs/2108.03234

Montag, C., Sindermann, C., Rozgonjuk, D., Yang, S., Elhai, J. D., & Yang, H. (2021). Investigating Links Between Fear of COVID-19, Neuroticism, Social Networks Use Disorder, and Smartphone Use Disorder Tendencies. Frontiers in Psychology, 0. https://doi.org/10.3389/fpsyg.2021.682837

https://en.wikipedia.org/wiki/Evidence_board

Expertise network effects

We’ve analyzed thousands of COVID-19 misinformation narratives. Here are six regional takeaways—Bulletin of the Atomic Scientists. (n.d.). Retrieved August 1, 2021, from https://thebulletin.org/2021/06/weve-analyzed-thousands-of-covid-19-misinformation-narratives-here-are-six-regional-takeaways/

Wang, B., Gou, M., Guo, Y., Tanaka, G., & Han, Y. (2020). Network structure-based interventions on spatial spread of epidemics in metapopulation networks. Physical Review E, 102(6), 062306. https://doi.org/10.1103/PhysRevE.102.062306

Sheetal, A., Feng, Z., & Savani, K. (2020). Using Machine Learning to Generate Novel Hypotheses: Increasing Optimism About COVID-19 Makes People Less Willing to Justify Unethical Behaviors. Psychological Science, 31(10), 1222–1235. https://doi.org/10.1177/0956797620959594

Ingale, M., & Shekatkar, S. M. (2020). Resource dependency and survivability in complex networks. Physical Review E, 102(6), 062304. https://doi.org/10.1103/PhysRevE.102.062304

Leah Keating on Twitter: “This work with @DavidJPOS and @gleesonj is now on arXiv (https://t.co/hxjZnCmKcM): ‘A multi-type branching process method for modelling complex contagion on clustered networks’ Here is a quick overview of our paper: (1/6) https://t.co/3jQ2flhk71” / Twitter. (n.d.). Retrieved July 23, 2021, from https://twitter.com/leahakeating/status/1418150117106978816

Gozzi, N., Scudeler, M., Paolotti, D., Baronchelli, A., & Perra, N. (2021). Self-initiated behavioral change and disease resurgence on activity-driven networks. Physical Review E, 104(1), 014307. https://doi.org/10.1103/PhysRevE.104.014307

Ortiz, E., & Serrano, M. Á. (2021). Multiscale opinion dynamics on real networks. ArXiv:2107.06656 [Physics]. http://arxiv.org/abs/2107.06656

Not usually one for this type of visual web, but I love this one for how it can be used, in addition to simply being interesting to see. Could be a great way to discover confirmation bias at play, if for instance people with opposing views are never referenced together. It could also simply serve as a way to find "other authors you might like," who write on similar topics to those you already have a founded interest in.

Herrera-Diestra, J. L., Tildesley, M., Shea, K., & Ferrari, M. (2021). Network structure and disease risk for an endemic infectious disease. ArXiv:2107.06186 [Physics, q-Bio]. http://arxiv.org/abs/2107.06186

Persoon, P. G. J. (2021). Cumulative structure and path length in networks of knowledge. ArXiv:2106.10480 [Physics]. http://arxiv.org/abs/2106.10480

Reis, E. F. dos, & Masuda, N. (2021). Metapopulation models imply non-Poissonian statistics of interevent times. ArXiv:2106.10348 [Physics]. http://arxiv.org/abs/2106.10348

Qian, Z.-Y., Yuan, C., Zhou, J., Chen, S.-M., & Nie, S. (2021). Optimal control of complex networks with conformity behavior. ArXiv:2106.10607 [Physics]. http://arxiv.org/abs/2106.10607

Li, X., Ostropolets, A., Makadia, R., Shoaibi, A., Rao, G., Sena, A. G., Martinez-Hernandez, E., Delmestri, A., Verhamme, K., Rijnbeek, P. R., Duarte-Salles, T., Suchard, M. A., Ryan, P. B., Hripcsak, G., & Prieto-Alhambra, D. (2021). Characterising the background incidence rates of adverse events of special interest for covid-19 vaccines in eight countries: Multinational network cohort study. BMJ, 373, n1435. https://doi.org/10.1136/bmj.n1435

Agarwal, A. (2021). Ripple Effect of a Pandemic: Analysis of the Psychological Stress Landscape during COVID19. PsyArXiv. https://doi.org/10.31234/osf.io/dm5x2

Agarwal, A. (2021). The Accidental Checkmate: Understanding the Intent behind sharing Misinformation on Social Media. PsyArXiv. https://doi.org/10.31234/osf.io/kwu58

Tybur, J. M., Lieberman, D., Fan, L., Kupfer, T. R., & de Vries, R. E. (2020). Behavioral Immune Trade-Offs: Interpersonal Value Relaxes Social Pathogen Avoidance. Psychological Science, 31(10), 1211–1221. https://doi.org/10.1177/0956797620960011

This is a great looking collection of articles on network science.

Gallacher, J., & Bright, J. (2021). Hate Contagion: Measuring the spread and trajectory of hate on social media. PsyArXiv. https://doi.org/10.31234/osf.io/b9qhd

Piotrowska, M. J., Sakowski, K., Karch, A., Tahir, H., Horn, J., Kretzschmar, M. E., & Mikolajczyk, R. T. (2020). Modelling pathogen spread in a healthcare network: Indirect patient movements. PLOS Computational Biology, 16(11), e1008442. https://doi.org/10.1371/journal.pcbi.1008442

Lutkenhaus, R. O., Jansz, J., & Bouman, M. P. A. (2019). Mapping the Dutch vaccination debate on Twitter: Identifying communities, narratives, and interactions. Vaccine: X, 1. https://doi.org/10.1016/j.jvacx.2019.100019

Grant, L., Hausman, B. L., Cashion, M., Lucchesi, N., Patel, K., & Roberts, J. (2015). Vaccination Persuasion Online: A Qualitative Study of Two Provaccine and Two Vaccine-Skeptical Websites. Journal of Medical Internet Research, 17(5), e4153. https://doi.org/10.2196/jmir.4153

Smith, N., & Graham, T. (2019). Mapping the anti-vaccination movement on Facebook. Information, Communication & Society, 22(9), 1310–1327. https://doi.org/10.1080/1369118X.2017.1418406

Karimi, Fariba, and Petter Holme. ‘A Temporal Network Version of Watts’s Cascade Model’. ArXiv:2103.13604 [Physics], 25 March 2021. http://arxiv.org/abs/2103.13604.

Holme, Petter, and Jari Saramäki. ‘Temporal Networks as a Modeling Framework’. ArXiv:2103.13586 [Physics], 24 March 2021. http://arxiv.org/abs/2103.13586.

Devriendt, K., Martin-Gutierrez, S., & Lambiotte, R. (2020). Variance and covariance of distributions on graphs. ArXiv:2008.09155 [Physics, Stat]. http://arxiv.org/abs/2008.09155

Di Lauro, F., Berthouze, L., Dorey, M. D., Miller, J. C., & Kiss, I. Z. (2020). The impact of network properties and mixing on control measures and disease-induced herd immunity in epidemic models: A mean-field model perspective. ArXiv:2007.06975 [Physics, q-Bio]. http://arxiv.org/abs/2007.06975

Brunson, E. K. (2013). The Impact of Social Networks on Parents’ Vaccination Decisions. Pediatrics, 131(5), e1397–e1404. https://doi.org/10.1542/peds.2012-2452

Wang, Xiangrong, Alejandro Tejedor, Yi Wang, and Yamir Moreno. ‘Unique Superdiffusion Induced by Directionality in Multiplex Networks’. ArXiv:2011.00991 [Physics], 2 November 2020. http://arxiv.org/abs/2011.00991.

Gupta, Prateek, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, et al. ‘COVI-AgentSim: An Agent-Based Model for Evaluating Methods of Digital Contact Tracing’. ArXiv:2010.16004 [Cs], 29 October 2020. http://arxiv.org/abs/2010.16004.

McCabe, Stefan, Leo Torres, Timothy LaRock, Syed Arefinul Haque, Chia-Hung Yang, Harrison Hartle, and Brennan Klein. ‘Netrd: A Library for Network Reconstruction and Graph Distances’. ArXiv:2010.16019 [Physics], 29 October 2020. http://arxiv.org/abs/2010.16019.

Hota, Ashish R., Tanya Sneh, and Kavish Gupta. ‘Impacts of Game-Theoretic Activation on Epidemic Spread over Dynamical Networks’. ArXiv:2011.00445 [Physics], 1 November 2020. http://arxiv.org/abs/2011.00445.

Heeren, A., HANSEEUW, B., Cougnon, L., & Lits, G. (2021, March 11). Excessive Worrying as the Driving Force of Anxiety During the First COVID-19 Lockdown-Phase in Belgium. https://doi.org/10.31234/osf.io/b34aj

Barrat, A., de Arruda, G. F., Iacopini, I., & Moreno, Y. (2021). Social contagion on higher-order structures. ArXiv:2103.03709 [Physics]. http://arxiv.org/abs/2103.03709

Fowler, J. H., & Christakis, N. A. (2008). Dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham Heart Study. BMJ, 337, a2338. https://doi.org/10.1136/bmj.a2338

Network integrity

Gordon, D. E., Hiatt, J., Bouhaddou, M., Rezelj, V. V., Ulferts, S., Braberg, H., Jureka, A. S., Obernier, K., Guo, J. Z., Batra, J., Kaake, R. M., Weckstein, A. R., Owens, T. W., Gupta, M., Pourmal, S., Titus, E. W., Cakir, M., Soucheray, M., McGregor, M., … Krogan, N. J. (2020). Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. Science, 370(6521). https://doi.org/10.1126/science.abe9403

Wang, X., Sirianni, A. D., Tang, S., Zheng, Z., & Fu, F. (2020). Public Discourse and Social Network Echo Chambers Driven by Socio-Cognitive Biases. Physical Review X, 10(4), 041042. https://doi.org/10.1103/PhysRevX.10.041042

Times info@bylinetimes.com (https://bylinetimes.com/), Byline. „Cambridge Analytica Psychologist Advising Global COVID-19 Disinformation Network Linked to Nigel Farage and Conservative Party“. Byline Times, 2. Februar 2021. https://bylinetimes.com/2021/02/02/cambridge-analytica-psychologist-advising-global-covid-19-disinformation-network-linked-to-nigel-farage-and-conservative-party/.

Hickok, A., Kureh, Y., Brooks, H. Z., Feng, M., & Porter, M. A. (2021). A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs. ArXiv:2102.06825 [Nlin, Physics:Physics]. http://arxiv.org/abs/2102.06825

This is an interesting and useful strategy. I've heard the idea several times before.

I'm curious if this is the oldest version of it? I have to imagine that there are earlier versions of it dating back to 2011 or 2012 if not earlier.

Ogbunu, B. C. (2020, October 27). The Science That Spans #MeToo, Memes, and Covid-19. Wired. https://www.wired.com/story/the-science-that-spans-metoo-memes-and-covid-19/

Nande A, Adlam B, Sheen J, Levy MZ, Hill AL (2021) Dynamics of COVID-19 under social distancing measures are driven by transmission network structure. PLoS Comput Biol 17(2): e1008684. https://doi.org/10.1371/journal.pcbi.1008684

Bericht über die Kündigung Lauren Wolfes durch die New York Times. Ist doppelt interessant—wegen der Problematik des Social Media Engagements von Journalistinnen und wegen der Aussagen zum Meinungsklima in den USA.

Impressed by this comment. It foreshadows what Roam would become:

• People think in terms of items belonging to multiple hierarchies
• If you allow users to describe items in a way that makes sense to them and allow them to search and browse by any of the terms they've used, you've solved many of the problems of existing file systems

What you need to build a complex information system is:

• Deep hierarchies for classifying things (overlapping hierarchies should be possible)
• Shallow hierarchies for noting relationships (Roam does this with a flat structure)
• Multi-values for every single field
• Controlled values (e.g. linking to other items when possible)

Maia, H. P., Ferreira, S. C., & Martins, M. L. (2020). Adaptive network approach for emergence of societal bubbles. ArXiv:2010.08635 [Nlin, Physics:Physics]. http://arxiv.org/abs/2010.08635

Centola, D. (n.d.). Why Social Media Makes Us More Polarized and How to Fix It. Scientific American. Retrieved October 25, 2020, from https://www.scientificamerican.com/article/why-social-media-makes-us-more-polarized-and-how-to-fix-it/