- Jan 2023
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www.complexityexplorer.org www.complexityexplorer.org
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https://www.youtube.com/watch?v=HwkRfN-7UWI
Seven Principles of Data Feminism
- Examine power
- Challenge power
- Rethink binaries and hierarchies
- Elevate emotion an embodiment
- Embrace pluralism
- Consider context
- Make labor visible
Abolitionist movement
There are some interesting analogies to be drawn between the abolitionist movement in the 1800s and modern day movements like abolition of police and racial justice, etc.
Topic modeling - What would topic modeling look like for corpuses of commonplace books? Over time?
wrt article: Soni, Sandeep, Lauren F. Klein, and Jacob Eisenstein. “Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers.” Journal of Cultural Analytics 6, no. 1 (January 18, 2021). https://doi.org/10.22148/001c.18841. - Brings to mind the difference in power and invisible labor between literate societies and oral societies. It's easier to erase oral cultures with the overwhelm available to literate cultures because the former are harder to see.
How to find unbiased datasets to study these?
aspirational abolitionism driven by African Americans in the 1800s over and above (basic) abolitionism
Tags
- watch
- algorithms
- emotional labor
- power frameworks
- Data Feminism
- Catherine D'Ignazio
- topic modeling
- invisible labor
- slavery
- defunding police
- abolitionists
- data science
- operationalization
- orality vs. literacy
- aspirational abolitionism
- dodging the memory hole
- intersectional feminism
- Lauren F. Klein
Annotators
URL
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- Jul 2021
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psyarxiv.com psyarxiv.com
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Lee, Y. K., Jung, Y., Lee, I., Park, J. E., & Hahn, S. (2021). Building a Psychological Ground Truth Dataset with Empathy and Theory-of-Mind During the COVID-19 Pandemic. PsyArXiv. https://doi.org/10.31234/osf.io/mpn3w
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- Sep 2020
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epjdatascience.springeropen.com epjdatascience.springeropen.com
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Adelani, D. I., Kobayashi, R., Weber, I., & Grabowicz, P. A. (2020). Estimating community feedback effect on topic choice in social media with predictive modeling. EPJ Data Science, 9(1), 1–23. https://doi.org/10.1140/epjds/s13688-020-00243-w
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- Jul 2020
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arxiv.org arxiv.org
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McQuillan, L., McAweeney, E., Bargar, A., & Ruch, A. (2020). Cultural Convergence: Insights into the behavior of misinformation networks on Twitter. ArXiv:2007.03443 [Physics]. http://arxiv.org/abs/2007.03443
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- Jun 2020
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psyarxiv.com psyarxiv.com
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Westrupp, E., Greenwood, C., Fuller-Tyszkiewicz, M., Berkowitz, T., Hagg, L., & Youssef, G. J. (2020). Text Mining of Reddit Posts: Using Latent Dirichlet Allocation to Identify Common Parenting Issues [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/cw54u
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arxiv.org arxiv.org
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link.springer.com link.springer.com
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Stokes, D. C., Andy, A., Guntuku, S. C., Ungar, L. H., & Merchant, R. M. (2020). Public Priorities and Concerns Regarding COVID-19 in an Online Discussion Forum: Longitudinal Topic Modeling. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-020-05889-w
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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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- Jul 2018
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course-computational-literary-analysis.netlify.com course-computational-literary-analysis.netlify.com
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Having heard the story of the past, my next inquiries (still inquiries after Rachel!) advanced naturally to the present time. Under whose care had she been placed after leaving Mr. Bruff’s house? and where was she living now?
Blake's account of Rachel is clearly distinct form the other narrators because of their romantic past. He mentions her frequently throughout his narrative. I would like to run a frequency count the number of times he mentions Rachel compared tot he rest of the narratives in the book. I wonder if it is possible to isolate the discussions of Rachel in each character's narrative and then do some topic modeling with the extracted texts to examine how Rachel is discussed by each character.
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- Aug 2017
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tedunderwood.com tedunderwood.com
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Computer scientists make LDA seem complicated because they care about proving that their algorithms work.
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