- Jul 2024
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en.wikipedia.org en.wikipedia.org
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https://en.wikipedia.org/wiki/Matthew_effect
The Matthew effect of accumulated advantage, sometimes called the Matthew principle, is the tendency of individuals to accrue social or economic success in proportion to their initial level of popularity, friends, and wealth. It is sometimes summarized by the adage or platitude "the rich get richer and the poor get poorer". The term was coined by sociologists Robert K. Merton and Harriet Zuckerman in 1968 and takes its name from the Parable of the Talents in the biblical Gospel of Matthew.
related somehow to the [[Lindy effect]]?
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- Jan 2024
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blogs.cornell.edu blogs.cornell.edu
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The Evaporative Cooling Effect describes the phenomenon that high value contributors leave a community because they cannot gain something from it, which leads to the decrease of the quality of the community. Since the people most likely to join a community are those whose quality is below the average quality of the community, these newcomers are very likely to harm the quality of the community. With the expansion of community, it is very hard to maintain the quality of the community.
via ref to Xianhang Zhang in Social Software Sundays #2 – The Evaporative Cooling Effect « Bumblebee Labs Blog [archived] who saw it
via [[Eliezer Yudkowsky]] in Evaporative Cooling of Group Beliefs
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- Aug 2023
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www.pewresearch.org www.pewresearch.org
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Health care is an area that will likely see many innovations. There are already multiple research prototypes underway looking at monitoring of one’s physical and mental health. Some of my colleagues (and myself as well) are also looking at social behaviors, and how those behaviors not only impact one’s health but also how innovations spread through one’s social network.
- for: quote, quote - Jason Hong, quote - health apps, health care app, idea spread through social network, mental health app, physical health app, transform app
- quote
- paraphrase
- Health care is an area that will likely see many innovations.
-There are already multiple research prototypes underway looking at monitoring of one’s
- physical and
- mental health.
- Some of my colleagues (and myself as well) are also looking at
- social behaviors, and how those behaviors
- not only impact one’s health but also
- how innovations spread through one’s social network.
- social behaviors, and how those behaviors
- Health care is an area that will likely see many innovations.
-There are already multiple research prototypes underway looking at monitoring of one’s
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- Jul 2023
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www.sciencedirect.com www.sciencedirect.com
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Measuring social presence in online-based learning: An exploratory path analysis using log data and social network analysis
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- Feb 2023
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eprints.soton.ac.uk eprints.soton.ac.uk
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This paper is relevant to understanding
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Learning
- it introduces me to a number of new useful concepts
- cognitive advantage
- cultural network analysis
- more detailed understanding of memetics
- cultural epidemiology
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- Dec 2022
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link.springer.com link.springer.com
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the spread of segregation, fads, revolts, protests, information on Twitter, and product marketing.
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link.springer.com link.springer.com
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We repeat this procedure 10,000 times. The value of 10,000 was selected because 9604 is the minimum size of samples required to estimate an error of 1 % with 95 % confidence [this is according to a conservative method; other methods also require <10,000 samples size (Newcombe 1998)]
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link.springer.com link.springer.com
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complex contagions, a type of social contagion which requires social reinforcement from multiple adopting neighbors.
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link.springer.com link.springer.com
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In this work, we develop the “Multi-Agent, Multi-Attitude” (MAMA) model which incorporates several key factors of attitude diffusion: (1) multiple, interacting attitudes; (2) social influence between individuals; and (3) media influence. All three components have strong support from the social science community.
several key factors of attitude diffusion: 1. multiple, interacting attitudes 2. social influence between individuals 3. media influence
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- Aug 2022
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www.aei.org www.aei.org
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Cox, D. A. (n.d.). Social isolation and community disconnection are not spurring conspiracy theories. American Enterprise Institute - AEI. Retrieved March 8, 2021, from https://www.aei.org/research-products/report/social-isolation-and-community-disconnection-are-not-spurring-conspiracy-theories/
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- Apr 2022
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www.zylstra.org www.zylstra.org
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3. Who are you annotating with? Learning usually needs a certain degree of protection, a safe space. Groups can provide that, but public space often less so. In Hypothes.is who are you annotating with? Everybody? Specific groups of learners? Just yourself and one or two others? All of that, depending on the text you’re annotating? How granular is your control over the sharing with groups, so that you can choose your level of learning safety?
This is a great question and I ask it frequently with many different answers.
I've not seen specific numbers, but I suspect that the majority of Hypothes.is users are annotating in small private groups/classes using their learning management system (LMS) integrations through their university. As a result, using it and hoping for a big social experience is going to be discouraging for most.
Of course this doesn't mean that no one is out there. After all, here you are following my RSS feed of annotations and asking these questions!
I'd say that 95+% or more of my annotations are ultimately for my own learning and ends. If others stumble upon them and find them interesting, then great! But I'm not really here for them.
As more people have begun using Hypothes.is over the past few years I have slowly but surely run into people hiding in the margins of texts and quietly interacted with them and begun to know some of them. Often they're also on Twitter or have their own websites too which only adds to the social glue. It has been one of the slowest social media experiences I've ever had (even in comparison to old school blogging where discovery is much higher in general use). There has been a small uptick (anecdotally) in Hypothes.is use by some in the note taking application space (Obsidian, Roam Research, Logseq, etc.), so I've seen some of them from time to time.
I can only think of one time in the last five or so years in which I happened to be "in a text" and a total stranger was coincidentally reading and annotating at the same time. There have been a few times I've specifically been in a shared text with a small group annotating simultaneously. Other than this it's all been asynchronous experiences.
There are a few people working at some of the social side of Hypothes.is if you're searching for it, though even their Hypothes.is presences may seem as sparse as your own at present @tonz.
Some examples:
@peterhagen Has built an alternate interface for the main Hypothes.is feed that adds some additional discovery dimensions you might find interesting. It highlights some frequent annotators and provide a more visual feed of what's happening on the public Hypothes.is timeline as well as data from HackerNews.
@flancian maintains anagora.org, which is like a planet of wikis and related applications, where he keeps a list of annotations on Hypothes.is by members of the collective at https://anagora.org/latest
@tomcritchlow has experimented with using Hypothes.is as a "traditional" comments section on his personal website.
@remikalir has a nice little tool https://crowdlaaers.org/ for looking at documents with lots of annotations.
Right now, I'm also in an Obsidian-based book club run by Dan Allosso in which some of us are actively annotating the two books using Hypothes.is and dovetailing some of this with activity in a shared Obsidian vault. see: https://boffosocko.com/2022/03/24/55803196/. While there is a small private group for our annotations a few of us are still annotating the books in public. Perhaps if I had a group of people who were heavily interested in keeping a group going on a regular basis, I might find the value in it, but until then public is better and I'm more likely to come across and see more of what's happening out there.
I've got a collection of odd Hypothes.is related quirks, off label use cases, and experiments: https://boffosocko.com/tag/hypothes.is/ including a list of those I frequently follow: https://boffosocko.com/about/following/#Hypothesis%20Feeds
Like good annotations and notes, you've got to put some work into finding the social portion what's happening in this fun little space. My best recommendation to find your "tribe" is to do some targeted tag searches in their search box to see who's annotating things in which you're interested.
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super-memory.com super-memory.com
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One of the most effective ways of enhancing memories is to provide them with a link to your personal life.
Personalizing ideas using existing memories is a method of brining new knowledge into one's own personal context and making them easier to remember.
link this to: - the pedagogical idea of context shifting as a means of learning - cards about reframing ideas into one's own words when taking notes
There is a solid group of cards around these areas of learning.
Random thought: Personal learning networks put one into a regular milieu of people who are talking and thinking about topics of interest to the learner. Regular discussions with these people helps one's associative memory by tying the ideas into this context of people with relation to the same topic. Humans are exceedingly good at knowing and responding to social relationships and within a personal learning network, these ties help to create context on an interpersonal level, but also provide scaffolding for the ideas and learning that one hopes to do. These features will tend to reinforce each other over time.
On the flip side of the coin there is anecdotal evidence of friends taking courses together because of their personal relationships rather than their interest in the particular topics.
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- Feb 2022
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psyarxiv.com psyarxiv.com
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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
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- Jan 2022
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arxiv.org arxiv.org
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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
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- Dec 2021
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arxiv.org arxiv.org
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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
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- Nov 2021
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link.aps.org link.aps.org
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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
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- Oct 2021
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www.mdpi.com www.mdpi.com
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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
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- Sep 2021
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link.aps.org link.aps.org
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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
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psyarxiv.com psyarxiv.com
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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
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- Aug 2021
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arxiv.org arxiv.org
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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
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www.frontiersin.org www.frontiersin.org
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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
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thebulletin.org thebulletin.org
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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/
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- Jul 2021
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journals.sagepub.com journals.sagepub.com
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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
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link.aps.org link.aps.org
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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
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Ortiz, E., & Serrano, M. Á. (2021). Multiscale opinion dynamics on real networks. ArXiv:2107.06656 [Physics]. http://arxiv.org/abs/2107.06656
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anagora.org anagora.org
Tags
Annotators
URL
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- May 2021
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psyarxiv.com psyarxiv.com
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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
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psyarxiv.com psyarxiv.com
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Agarwal, A. (2021). The Accidental Checkmate: Understanding the Intent behind sharing Misinformation on Social Media. PsyArXiv. https://doi.org/10.31234/osf.io/kwu58
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journals.sagepub.com journals.sagepub.com
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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
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psyarxiv.com psyarxiv.com
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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
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- Apr 2021
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www.sciencedirect.com www.sciencedirect.com
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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
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www.jmir.org www.jmir.org
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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
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www.tandfonline.com www.tandfonline.com
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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
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- Mar 2021
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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.
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pediatrics.aappublications.org pediatrics.aappublications.org
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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
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arxiv.org arxiv.org
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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
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www.bmj.com www.bmj.com
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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
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- Feb 2021
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link.aps.org link.aps.org
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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
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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
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www.wired.com www.wired.com
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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/
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journals.plos.org journals.plos.org
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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
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Economists call this a "network effect": the more people there are on Twitter, the more reason there is to be on Twitter and the harder it is to leave. But technologists have another name for this: "lock in." The more you pour into Twitter, the more it costs you to leave. Economists have a name for that cost: the "switching cost."
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- Oct 2020
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www.scientificamerican.com www.scientificamerican.com
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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/
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covid-19.iza.org covid-19.iza.org
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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/
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blog.joinmastodon.org blog.joinmastodon.org
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So that’s already a huge advantage over other platforms due the basic design. And in my opinion it’s got advantages over the other extreme, too, a pure peer-to-peer design, where everyone would have to fend for themselves, without the pooled resources.
Definitely something the IndieWeb may have to solve for.
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Mastodon deliberately does not support arbitrary search. If someone wants their message to be discovered, they can use a hashtag, which can be browsed. What does arbitrary search accomplish? People and brands search for their own name to self-insert into conversations they were not invited to. What you can do, however, is search messages you posted, received or favourited. That way you can find that one message on the tip of your tongue.
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appliednetsci.springeropen.com appliednetsci.springeropen.com
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First, I will focus in these larger groups because reviews that transcend the boundary between the social and natural sciences are rare, but I believe them to be valuable. One such review is Borgatti et al. (2009), which compares the network science of natural and social sciences arriving at a similar conclusion to the one I arrived.
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Houghton, J. P. (2020). Interdependent Diffusion: The social contagion of interacting beliefs. ArXiv:2010.02188 [Physics]. http://arxiv.org/abs/2010.02188
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arxiv.org arxiv.org
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Merlino, L. P., Pin, P., & Tabasso, N. (2020). Debunking Rumors in Networks. ArXiv:2010.01018 [Physics]. http://arxiv.org/abs/2010.01018
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www.deutschlandfunk.de www.deutschlandfunk.de
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Ausführliche Sendung über Desinformationstechniken vor allem im Umkreis der Trump-Kampagne, viele Hinweise auf weitere Ressourcen
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- Sep 2020
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Romanini, Daniele, Sune Lehmann, and Mikko Kivelä. ‘Privacy and Uniqueness of Neighborhoods in Social Networks’. ArXiv:2009.09973 [Physics], 21 September 2020. http://arxiv.org/abs/2009.09973.
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Ehlert, A., Kindschi, M., Algesheimer, R., & Rauhut, H. (2020). Human social preferences cluster and spread in the field. Proceedings of the National Academy of Sciences, 117(37), 22787–22792. https://doi.org/10.1073/pnas.2000824117
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psyarxiv.com psyarxiv.com
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Lee, Hyeon-seung, Derek Dean, Tatiana Baxter, Taylor Griffith, and Sohee Park. ‘Deterioration of Mental Health despite Successful Control of the COVID-19 Pandemic in South Korea’. Preprint. PsyArXiv, 30 August 2020. https://doi.org/10.31234/osf.io/s7qj8.
Tags
- loneliness
- psychosis-risk
- COVID-19
- stress
- social factors
- nationwide lockdown
- lang:en
- physical health
- crisis
- public health
- females
- social distancing
- demographic
- South Korea
- is:preprint
- behavioural science
- anxiety
- general population
- depression
- psychological outcome
- social network
- mental health
Annotators
URL
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www.scientificamerican.com www.scientificamerican.com
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Stix, G. (n.d.). Zoom Psychiatrists Prep for COVID-19’s Endless Ride. Scientific American. Retrieved June 9, 2020, from https://www.scientificamerican.com/article/zoom-psychiatrists-prep-for-covid-19s-endless-ride1/
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www.pmo.gov.sg www.pmo.gov.sg
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katherine_chen. (2020, June 17). PMO | National Broadcast by PM Lee Hsien Loong on 7 June 2020 [Text]. Prime Minister’s Office Singapore; katherine_chen. http://www.pmo.gov.sg/Newsroom/National-Broadcast-PM-Lee-Hsien-Loong-COVID-19
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r/BehSciAsk—Integrating Behavioural Science into Epidimiology. (n.d.). Reddit. Retrieved June 27, 2020, from https://www.reddit.com/r/BehSciAsk/comments/hg501h/integrating_behavioural_science_into_epidimiology/
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www.visualcapitalist.com www.visualcapitalist.com
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Ali, A. (2020, August 28). Visualizing the Social Media Universe in 2020. Visual Capitalist. https://www.visualcapitalist.com/visualizing-the-social-media-universe-in-2020/
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- Aug 2020
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www.pnas.org www.pnas.org
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Thurner, S., Klimek, P., & Hanel, R. (2020). A network-based explanation of why most COVID-19 infection curves are linear. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2010398117
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link.aps.org link.aps.org
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Perez, I. A., Di Muro, M. A., La Rocca, C. E., & Braunstein, L. A. (2020). Disease spreading with social distancing: A prevention strategy in disordered multiplex networks. Physical Review E, 102(2), 022310. https://doi.org/10.1103/PhysRevE.102.022310
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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
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Holtz, D., Zhao, M., Benzell, S. G., Cao, C. Y., Rahimian, M. A., Yang, J., Allen, J., Collis, A., Moehring, A., Sowrirajan, T., Ghosh, D., Zhang, Y., Dhillon, P. S., Nicolaides, C., Eckles, D., & Aral, S. (2020). Interdependence and the cost of uncoordinated responses to COVID-19. Proceedings of the National Academy of Sciences, 117(33), 19837–19843. https://doi.org/10.1073/pnas.2009522117
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link.aps.org link.aps.org
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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
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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
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www.nature.com www.nature.com
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Shahal, S., Wurzberg, A., Sibony, I., Duadi, H., Shniderman, E., Weymouth, D., Davidson, N., & Fridman, M. (2020). Synchronization of complex human networks. Nature Communications, 11(1), 3854. https://doi.org/10.1038/s41467-020-17540-7
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www.nber.org www.nber.org
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Kuchler, T., Russel, D., & Stroebel, J. (2020). The Geographic Spread of COVID-19 Correlates with Structure of Social Networks as Measured by Facebook (Working Paper No. 26990; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w26990
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Alfaro, L., Faia, E., Lamersdorf, N., & Saidi, F. (2020). Social Interactions in Pandemics: Fear, Altruism, and Reciprocity (Working Paper No. 27134; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27134
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Acemoglu, Daron, Ali Makhdoumi, Azarakhsh Malekian, and Asuman Ozdaglar. ‘Testing, Voluntary Social Distancing and the Spread of an Infection’. Working Paper. Working Paper Series. National Bureau of Economic Research, July 2020. https://doi.org/10.3386/w27483.
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Simchon, A., Brady, W. J., & Bavel, J. J. V. (2020). Troll and Divide: The Language of Online Polarization. https://doi.org/10.31234/osf.io/xjd64
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journals.plos.org journals.plos.org
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Aleta, A., Arruda, G. F. de, & Moreno, Y. (2020). Data-driven contact structures: From homogeneous mixing to multilayer networks. PLOS Computational Biology, 16(7), e1008035. https://doi.org/10.1371/journal.pcbi.1008035
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Akbarpour, M., Cook, C., Marzuoli, A., Mongey, S., Nagaraj, A., Saccarola, M., Tebaldi, P., Vasserman, S., & Yang, H. (2020). Socioeconomic Network Heterogeneity and Pandemic Policy Response (Working Paper No. 27374; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27374
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- Jul 2020
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nautil.us nautil.us
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West, D. K. & G. (2020, July 8). The Damage We’re Not Attending To. Nautilus. http://nautil.us/issue/87/risk/the-damage-were-not-attending-to
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osf.io osf.io
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Starominski-Uehara, M. (2020). Powering Social Media Footage: Simple Guide for the Most Vulnerable to Make Emergency Visible [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/gefhv
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Laliotis, I., & Minos, D. (2020). Spreading the disease: The role of culture [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/z4ndc
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www.youtube.com www.youtube.com
-
Thomas W. Malone—COVID-19 and Collective Intelligence (ACM CI’20). (n.d.). Retrieved June 25, 2020, from https://www.youtube.com/watch?v=W5RfAZMMTPM
-
-
www.youtube.com www.youtube.com
-
Jeff Howe - Crowdsourcing and the Crisis: Collective Intelligence in the Age of Covid-19 (ACM CI’20). (n.d.). Retrieved June 25, 2020, from https://www.youtube.com/watch?v=POPMMHyIoS0
-
-
osf.io osf.io
-
Starominski-Uehara, M. (2020). Powering Social Media Footage: Simple Guide for the Most Vulnerable to Make Emergency Visible [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/ek6tz
-
-
osf.io osf.io
-
Weeden, K. A., & Cornwell, B. (2020). The Small World Network of College Classes: Implications for Epidemic Spread on a University Campus [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/n5gw4
-
-
arxiv.org arxiv.org
-
Allard, A., Moore, C., Scarpino, S. V., Althouse, B. M., & Hébert-Dufresne, L. (2020). The role of directionality, heterogeneity and correlations in epidemic risk and spread. ArXiv:2005.11283 [Physics, q-Bio]. http://arxiv.org/abs/2005.11283
-
-
www.nature.com www.nature.com
-
Block, P., Hoffman, M., Raabe, I. J., Dowd, J. B., Rahal, C., Kashyap, R., & Mills, M. C. (2020). Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nature Human Behaviour, 4(6), 588–596. https://doi.org/10.1038/s41562-020-0898-6
-
-
-
Mann, P., Smith, V. A., Mitchell, J. B. O., & Dobson, S. (2020). Two-pathogen model with competition on clustered networks. ArXiv:2007.03287 [Physics, q-Bio]. http://arxiv.org/abs/2007.03287
-
-
arxiv.org arxiv.org
-
Lovato, J., Allard, A., Harp, R., & Hébert-Dufresne, L. (2020). Distributed consent and its impact on privacy and observability in social networks. ArXiv:2006.16140 [Physics]. http://arxiv.org/abs/2006.16140
-
-
psyarxiv.com psyarxiv.com
-
Chambon, M., Dalege, J., Elberse, J., & van Harreveld, F. (2020). A psychological network approach to factors related to preventive behaviors during pandemics: A European COVID-19 study [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/es45v
-
-
www.nature.com www.nature.com
-
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
-
- Jun 2020
-
psyarxiv.com psyarxiv.com
-
Yucel, M., Sjobeck, G., Glass, R., & Rottman, J. (2020). Gossip, Sabotage, and Friendship Network Dataset [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/m6tsx
-
-
psyarxiv.com psyarxiv.com
-
Ekstrom, P. D., & Lai, C. K. (2020, June 18). The Selective Communication of Political Information. https://doi.org/10.31234/osf.io/pnr9u
-
-
-
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
-
-
psyarxiv.com psyarxiv.com
-
Borsboom, D., Blanken, T., Dablander, F., Tanis, C., van Harreveld, F., & van Mieghem, P. (2020). BECON methodology [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/53ey9
-
-
www.sciencedirect.com www.sciencedirect.com
-
Camacho, D., Panizo-LLedot, Á., Bello-Orgaz, G., Gonzalez-Pardo, A., & Cambria, E. (2020). The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools. Information Fusion. https://doi.org/10.1016/j.inffus.2020.05.009
-
-
journals.sagepub.com journals.sagepub.com
-
Maltby, J., Hunt, S. A., Ohinata, A., Palmer, E., & Conroy, S. (2020). Frailty and Social Isolation: Comparing the Relationship between Frailty and Unidimensional and Multifactorial Models of Social Isolation: Journal of Aging and Health. https://doi.org/10.1177/0898264320923245
-
-
www.nature.com www.nature.com
-
McAvoy, A., Allen, B., & Nowak, M. A. (2020). Social goods dilemmas in heterogeneous societies. Nature Human Behaviour, 1–13. https://doi.org/10.1038/s41562-020-0881-2
-
-
-
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
-
-
www.sciencedirect.com www.sciencedirect.com
-
Zhou, B., Lu, X., & Holme, P. (2020). Universal evolution patterns of degree assortativity in social networks. Social Networks, 63, 47–55. https://doi.org/10.1016/j.socnet.2020.04.004
-