Measuring social presence in online-based learning: An exploratory path analysis using log data and social network analysis
21 Matching Annotations
- Jul 2023
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www.sciencedirect.com www.sciencedirect.com
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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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- 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.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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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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- Sep 2020
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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.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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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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- 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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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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- 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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- 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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- 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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- 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 2016
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googleguacamole.wordpress.com googleguacamole.wordpress.com
- 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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