- 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
- mental health
- social network
- crisis
- social distancing
- behavioural science
- stress
- demographic
- females
- loneliness
- general population
- depression
- public health
- social factors
- nationwide lockdown
- anxiety
- lang:en
- COVID-19
- physical health
- psychological outcome
- is:preprint
- South Korea
- psychosis-risk
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
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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
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www.youtube.com www.youtube.com
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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
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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/ek6tz
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osf.io osf.io
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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
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arxiv.org arxiv.org
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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
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www.nature.com www.nature.com
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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
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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
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arxiv.org arxiv.org
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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
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psyarxiv.com psyarxiv.com
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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
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www.nature.com www.nature.com
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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
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- Jun 2020
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psyarxiv.com psyarxiv.com
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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
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psyarxiv.com psyarxiv.com
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Ekstrom, P. D., & Lai, C. K. (2020, June 18). The Selective Communication of Political Information. https://doi.org/10.31234/osf.io/pnr9u
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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
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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
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www.sciencedirect.com www.sciencedirect.com
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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
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journals.sagepub.com journals.sagepub.com
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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
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www.nature.com www.nature.com
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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
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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
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www.sciencedirect.com www.sciencedirect.com
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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
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Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020). Echo Chambers on Social Media: A comparative analysis. ArXiv:2004.09603 [Physics]. http://arxiv.org/abs/2004.09603
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Gou, W., Huang, S., Chen, J., Li, X., & Chen, Q. (2020). Structural and Dynamic of Global Population Migration Network. ArXiv:2006.02208 [Physics]. http://arxiv.org/abs/2006.02208
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Poiitis, M., Vakali, A., & Kourtellis, N. (2020). On the Aggression Diffusion Modeling and Minimization in Online Social Networks. ArXiv:2005.10646 [Physics]. http://arxiv.org/abs/2005.10646
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Mariani, M. S., & Lü, L. (2020). Network-based ranking in social systems: Three challenges. Journal of Physics: Complexity, 1(1), 011001. https://doi.org/10.1088/2632-072X/ab8a61
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- May 2020
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psyarxiv.com psyarxiv.com
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Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (2020, April 14). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. https://doi.org/10.31234/osf.io/tfs7c
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www.wired.com www.wired.com
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Porter, E. & Wood. T.J. (2020 May 14). Why is Facebook so afraid of checking facts? Wired. https://www.wired.com/story/why-is-facebook-so-afraid-of-checking-facts/
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psyarxiv.com psyarxiv.com
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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
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journals.sagepub.com journals.sagepub.com
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Batty, M. (2020). The Coronavirus crisis: What will the post-pandemic city look like?: Environment and Planning B: Urban Analytics and City Science, 47(4), https://doi.org/10.1177/2399808320926912
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www.nature.com www.nature.com
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Ball, P. (2020). Anti-vaccine movement could undermine efforts to end coronavirus pandemic, researchers warn. Nature, 581(7808), 251–251. https://doi.org/10.1038/d41586-020-01423-4
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Pichler, Anton, Marco Pangallo, R. Maria del Rio-Chanona, François Lafond, and J. Doyne Farmer. “Production Networks and Epidemic Spreading: How to Restart the UK Economy?” ArXiv:2005.10585 [Physics, q-Fin], May 21, 2020. http://arxiv.org/abs/2005.10585.
Tags
- reopening industry
- social distincing
- economics
- consumption
- supply
- transmission rate
- work from home
- input-output constraints
- demand
- unemployment
- epidemiology
- inventory dynamics
- production
- lang:en
- COVID-19
- United Kingdom
- GDP
- production network
- epidemic spreading
- industry
- economic growth
- is:article
Annotators
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Almaatouq, A., Noriega-Campero, A., Alotaibi, A., Krafft, P. M., Moussaid, M., & Pentland, A. (2020). Adaptive social networks promote the wisdom of crowds. Proceedings of the National Academy of Sciences, 201917687. https://doi.org/10.1073/pnas.1917687117
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link.aps.org link.aps.org
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Vasques Filho, D., & O’Neale, D. R. J. (2020). Transitivity and degree assortativity explained: The bipartite structure of social networks. Physical Review E, 101(5), 052305. https://doi.org/10.1103/PhysRevE.101.052305
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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
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Liu, L., Wang, X., Tang, S., & Zheng, Z. (2020). Complex social contagion induces bistability on multiplex networks. ArXiv:2005.00664 [Physics]. http://arxiv.org/abs/2005.00664
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twitter.com twitter.com
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psyarxiv.com psyarxiv.com
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Elmer, T., Mepham, K., & Stadtfeld, C. (2020). Students under lockdown: Assessing change in students’ social networks and mental health during the COVID-19 crisis [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/ua6tq
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Vasiliauskaite, V., & Rosas, F. E. (2020). Understanding complexity via network theory: A gentle introduction. ArXiv:2004.14845 [Physics]. http://arxiv.org/abs/2004.14845
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- Apr 2020
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Chaves, M. S., Mattos, T. G., & Atman, A. P. F. (2020). Characterizing network topology using first-passage analysis. Physical Review E, 101(4), 042123. https://doi.org/10.1103/PhysRevE.101.042123
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lists.ufl.edu lists.ufl.edu
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LISTSERV 16.0—SOCNET Archives. (n.d.). Retrieved April 20, 2020, from https://lists.ufl.edu/cgi-bin/wa?A2=ind2004&L=SOCNET&P=9667
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- Dec 2019
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runyourown.social runyourown.social
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Social solutions to social problems This document exists to lay out some general principles of running a small social network site that have worked for me. These principles are related to community building more than they are related to specific technologies. This is because the big problems with social network sites are not technical: the problems are social problems related to things like policy, values, and power.
Social solutions to social problems
Tags
Annotators
URL
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- Aug 2019
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runyourown.social runyourown.social
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Running a small social network is like hosting a party. It requires social intelligence, empathy, and yes, technical skills.
Testing out this Hypothesis thing
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Annotators
URL
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- Aug 2018
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www.futurelearn.com www.futurelearn.com
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You might have seen the hashtag #BlackLivesMatter in the previous step. In this 6-minute video, #BlackTwitter after #Ferguson, we meet activists who were involved in the movement and learn about their own uses of Twitter as a platform of protest. Hashtags, when used like this, can be extremely complex in the way they represent ideas, communities and individuals.
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wendynorris.com wendynorris.com
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Because of both the content that people upload and the behavioral traces that they leavebehind, social network sites have unprecedented quantities of data concerning humaninteraction. This presents unique opportunities and challenges. On one hand, SNSs offera vibrant “living lab” and access to behavioral data at a scale inconceivable to manysocial scientists. On the other, the data that are available present serious research ethicsquestions and introduce new types of biases that must be examined (boyd and Crawford2012)
The scope and scale of trace data —from settings, public facing fatures, and server-side — presents similar challenges as technological platform changes = new ethics/privacy issues.
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For those of us who believe that social network sites are socio-technical systems, in whichsocial and technical factors shape one another, failing to describe the site under studyignores the fact that the technological constraints and affordances of a site will shapeuser practices and that social norms will emerge over time. Not including informationabout what the feature set was at the time of data collection forecloses the possibility ofidentifying patterns that emerge over time and through the accumulated scholarshipacross a range of sites and user samples. Unfortunately, because they have no knowledgeabout how things will continue to evolve and which features will becomeimportant to track, researchers may not be able to identify the salient features to reportand may struggle with devoting scarce publication space to these details, but this doesn’tundermine the importance of conscientious consideration towards describing the artifactbeing analyzed.
What about documenting technological features/artifacts on a stand-alone website or public repository, like Github to account for page limits?
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In order to produce scholarship that will be enduring, the onus is on social mediaresearchers to describe the technological artifact that they are analyzing with as muchcare as survey researchers take in describing the population sampled, and with as muchdetail as ethnographers use when describing their field site. This is not to say thatresearchers must continue to describe technologies as if no one knows what they are—weare beyond the point where researchers must explain how electronic mail or “email” islike or unlike postal mail. But, rather, researchers must clearly describe the socio-technical context of the particular site, service, or application their scholarship isaddressing. In addition to attending to the technology itself, and the interchange betweentechnical and social processes, we believe SNS researchers should make a concertedeffort to include the date of data collection and to describe the site at the moment of datacollection and the relevant practices of its users. These descriptions will enable laterresearchers to synthesize across studies to identify patterns, much in the same wayreporting exact effect sizes allows for future meta-analyses
Excellent point and important for my SBTF studies.
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One key challenge of studying social media is that designers of these tools are innovatingat a very rapid timeframe and often with little advance notice. Given the rapidly changinginfrastructure and the timeframe of academic publishing, the site at the time of datacollection is likely to be very different from its incarnation at the point of publication.
Challenges of studying SNSs:
Temporal effects of platform changes.
Later in the passage, the authors encourage researchers to fully describe the SNS/platform features studied and any potential effects on user behavior, practices, and norms to avoid orphaned research.
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Because of howpeople's position within the SNS shapes their experiences of it, activity-centric analysesrequire contextualization and translation, not unlike what social scientists studyingdiffering cultural practices have had to do for decades.
Challenges of studying SNSs:
User's position with the social graph shapes experience and interactions.
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What oneexperiences on SNSs and the content to which one is exposed differs depending on thestructure of one's network, a user's individual preferences and history, and her activitiesat that moment.
Challenges of studying SNSs:
Content varies by network structure, preferences, history and user activity -- but also site technology/upgrades/new features/deprecated features.
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By far the most pressing challengefor SNS scholars lies in the rapid pace at which innovations and technical changes areimplemented in this space. For scholarship in this arena to develop, SNS researchersneed to be mindful of the ways in which these sites evolve over time and the effects thismay have on the interpersonal, psychological, and sociological processes they arestudying.
Challenges of studying SNSs.
Evolution of site and the way people use it.
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What makes “social media” significant as a category is not the technology, butrather the socio-technical dynamics that unfolded as millions of people embraced thetechnology and used it to collaborate, share information, and socialize. Popular genres ofsocial media integrated the public nature of interest-driven CMC with the more intimatedynamics of interpersonal CMC.
I'm curious why the authors don't mention the UI/UX advancements in SNS that allowed non-technical people to participate online, rather than passively read. Even most blogs in the early 00s were challenging to use, let alone publish on, without some technical savvy.
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All SNSs support multiple modes of communication: one-to-many and one-to-one,synchronous and asynchronous, textual and media-based
This functionality is the make-or-break for collecting user-generated content during humanitarian crises by DHNs.
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Many of the weak tie relationships articulatedon SNSs would fade away were it not for the ease with which people can communicate,share, and maintain simple connections. For this reason, this new definition positionssocial network sites first and foremost as a communication platform, while alsohighlighting the importance of sharing content, typically consumed through a stream.
Evolution of the new definition of social network site emphasizes its use as a communication platform, followed by content sharing.
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A social network site is anetworked communication platformin which participants1) haveuniquely identifiable profilesthat consist of user-supplied content, contentprovided by other users, and/or system-level data; 2) canpublicly articulateconnectionsthat can be viewed and traversed by others; and 3) can consume,produce, and/or interact withstreams of user-generated contentprovided by theirconnections on the site.
Updated social network site definition.
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As social network sites have become mainstream, traversing the connections betweenpeople to view profiles is no longer the sole—or, even primary—way of participation.Content is surfaced through streams, and each piece of content is embedded withnumerous links to other content nuggets.
Streamed content has supplanted the social graph for traversing SNSs.
Like the API robots, this also contributes to mis/disinformation campaigns that influence on- and offline behavior.
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Yet, one significant shift has unfolded: the traversability ofconnections has become more important for machines than users. As APIs make thesocial graph available to broader audiences, algorithms are being designed to traversethe graph and learn about the individual nodes’ relationship to one another.
For the SNS, crawlers help serve recommended content, ads, search, and drive prediction models.
Also, very likely contributes to ease of launching mis/disinformation campiagns.
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The ability to see—andtraverse—others’ contact lists was innovative and important in several ways. From anadoption perspective, it enabled users to find shared contacts easily, thus lowering thebarriers to initiating contact with other users and enabling users to harness networkeffects more easily. From a social perspective, it allowed people to easily see therelationships between others, to reconnect with old friends and acquaintances, and totravel through the network in a way that enhanced social interactions.
Value of viewing/traversing connections.
Early on, this capacity was a critical and defining feature. The default site design is to "display one's articulated network..."
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The rise of open APIs and developer platforms meant that these collections of articulatedcontacts became valuable in contexts outside that particular SNS. Engineers andentrepreneurs alike began talking about the “social graph”—the global network oflinkages between all individuals within a system (Fitzpatrick and Recordon2007). Thislanguage emerged at a time when commercial entities began to believe that the socialgraph hadvalue beyond the individual's relationship with a given social networksite.
Social graph definition.
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As SNSs became more popular with a wider range ofindividuals, many individuals’ contact lists became more diverse as these users Friendedpeople representing a range of contexts (family, professional contacts, church members,etc.). This growing diversity has contributed to cases of “context collapse,” whichdescribes the ways in which individuals that we know from different social contexts cometogether in SNSs in potentially uncomfortable ways (Marwick and boyd2011)
Context collapse definition.
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For users, these connections represent what sociologistsrefer to as a person'ssocial network—the collection of social relations of varyingstrengths and importance that a person maintains
Social network definition.
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Earlier communication tools enabled individuals to create a private list ofcontacts (for instance a buddy list on instant messaging), to establish a group of contactsthat were shared by others (such as a listserv membership list), or to publish a list ofrelated links (such as a blogroll), but SNSs extended the practice of creating a publiclyvisible, personally curated list of contacts and made it a mainstream practice.
Differences between SNS and CMC.
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Streams of quotidian,ephemeral content encourage people to participate more in that they provide an initialartifact around which others can engage. Features that support actions associated withstatus updates—the ability to post comments to, share, or register interest in an update—also encourage a stream of activity that is prompted by an update but often takes on a lifeof its own in the central stream. Today's SNSs are more like news aggregators than theyare like profile-based contexts, even if the algorithm for displaying content is quiteobfuscated.
Essentially, this is the hook to motivate user-generated content.
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In boyd and Ellison (2007), we attempted to stabilize the discussion by offeringa definition of social network sites:web-based services that allow individuals to (1) construct a public or semi-publicprofile within a bounded system, (2) articulate a list of other users with whom theyshare a connection, and (3) view and traverse their list of connections and thosemade by others within the system.
Early definition of social network sites. Later Ellison and boyd redefine SNS per evolving Web 2.0 standards, CMC studies and social norms.
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- Jul 2018
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globalvoices.org globalvoices.org
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Then I used Gephi, another free data analysis tool, to visualize the data as an entity-relationship graph. The coloured circles—called Nodes—represent Twitter accounts, and the intersecting lines—known as Edges—refer to Follow/Follower connections between accounts. The accounts are grouped into colour-coded community clusters based on the Modularity algorithm, which detects tightly interconnected groups. The size of each node is based on the number of connections that account has with others in the network.
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Using the open-source NodeXL tool, I collected and imported a complete list of accounts tweeting that exact phrase into a spreadsheet. From that list, I also gathered and imported an extended community of Twitter users, comprised of the friends and followers of each account. It was going to be an interesting test: if the slurs against Nemtsov were just a minor case of rumour-spreading, they probably wouldn't be coming from more than a few dozen users.
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- Apr 2018
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www.cbc.ca www.cbc.ca
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"Similarly on the justice front, we now work with a Legal Aid funded lawyer. If I see that someone has legal concerns of any sort — and these can be very broad from domestic violence issues to family issues to criminal issues — I can refer them straight over to that member of our team."
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www.theatlantic.com www.theatlantic.com
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This fall, my colleagues and I released gobo.social, a customizable news aggregator. Gobo presents you with posts from your friends, but also gives you a set of sliders that govern what news you see and what’s hidden from you. Want more serious news, less humor? Move a slider. Need to hear more female voices? Adjust the gender slider, or press the “mute all men” button for a much quieter internet. Gobo currently includes half a dozen ways to tune your news feed, with more to come.
Gobo, a proof of concept.
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- Mar 2018
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At the moment, several projects in the space are working to adopt new supplementary protocols, with the intent of building better bridges between one another. The proposed development might end up looking like this:<img class="progressiveMedia-noscript js-progressiveMedia-inner" src="https://via.hypothes.is/im_/https://cdn-images-1.medium.com/max/1600/1*3pEK-Fwq7bNOVcnXfVdNuQ.png">Diaspora at this time has no plans for new protocols, having just significantly upgraded its own. postActiv intends to adopt support for Diaspora federation in a future release. Mastodon just released support for ActivityPub, and Pleroma , Socialhome and GNU Social are thinking of adopting it. Nextcloud is also notably getting into the federation space, and Hubzilla and Friendica will likely both support the ActivityPub protocol as extensions.
Where we discover that Friendica (and Hubzilla) are clearly the best options for navigating The Free Network.
It's a shame that the connectivity to Twitter and other non-free networks and services is not better highlighted. It's clearly by being compatible with the non-free networks that the Free Network will win in the end -- by allowing people to escape en masse.
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- May 2017
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Interesting things are happening over at Mastodon. If you have had your ears tuned to the hacker grapevines, you will most likely have heard that Mastodon is an open source federated social network that works very much like Twitter but is, in fact, not Twitter, and thus poses a challenge to the venerable bird site.
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- Mar 2017
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tachesdesens.blogspot.com tachesdesens.blogspot.com
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I arrived in class to find our partner librarians ready to teach our students.
Colleagues. connection. mutualisation.
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Meeting new colleagues with whom I could have fun teaching was high up on my 'would love to' list.
Network colleagues
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tachesdesens.blogspot.com tachesdesens.blogspot.com
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Then it was Maha's Birthday, why don't we sing 'Happy Birthday' I thought, - well why not?
Distant Presence Friends
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- Jan 2017
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www.insidehighered.com www.insidehighered.com
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Asking questions via social media that are intentionally designed to elicit responses can provide a plethora of useful responses. Why wait until an end-of-year survey to find out about an issue when you can poll/question students throughout the year via social media?
It doesn't have to be just student feedback about the operations and mechanics of the course, or as a replacement for a course survey tool. You can also use the platform as a way to engage students on the content relevant to the learning outcomes of the course. And use the platform to connect learners with people in the field of study.
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- Apr 2016
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googleguacamole.wordpress.com googleguacamole.wordpress.com
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followed a TAGS Explorer of a conference hashtag
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- Jan 2016
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clintlalonde.net clintlalonde.net
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insert reflective pause to acknowledge the power of weak tie networks here
And references to Granovetter, with his famous 1973 article. This [article on sports media and Twitter](http://www.cabdirect.org/abstracts/20143219270.html sounds contextually relevant.
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- Dec 2015
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essentiallyshannon.blogspot.com essentiallyshannon.blogspot.com
- Jul 2015
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blogs.lse.ac.uk blogs.lse.ac.uk
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academia.edu
Curiously, I couldn't see this service in the above comparison table. Is it hidden and grouped under "ResearchGate &..."?
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scienceoftheinvisible.blogspot.com scienceoftheinvisible.blogspot.com
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Whether or not you take a constructivist view of education, feedback on performance is inevitably seen as a crucial component of the process. However, experience shows that students (and academic staff) often struggle with feedback, which all too often fails to translate into feed-forward actions leading to educational gains. Problems get worse as student cohort sizes increase. By building on the well-established principle of separating marks from feedback and by using a social network approach to amplify peer discussion of assessed tasks, this paper describes an efficient system for interactive student feedback. Although the majority of students remain passive recipients in this system, they are still exposed to deeper reflection on assessed tasks than in traditional one-to-one feedback processes.
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