Editing Advanced Computational Communication Methods (Summer 2023) (section)

= Schedule =

'''NOTE''':  This section will be modified throughout the course to meet the class's needs. Check back in often.

Each week will include the topic of the week. This is where we will gather and organize resources regarding each topic. Please '''be bold''' in editing this portion of the wiki to add or arrange resources.


== Week 1: Welcome! (May 16) ==

'''Assignment Due:''' 
* None

'''Required Readings:''' 


'''Agenda:'''
* Class overview and expectations — We'll walk through this syllabus.
* Make assignments for topic exploration

'''Slides:'''

[https://jeremydfoote.com/computational_communication_resources/welcome_slides/lecture/welcome.html#/welcome-to-com-682 Welcome slides]

== Week 2: Reproducible Research I (May 23) ==

'''Resources:'''

* Paper: Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK (2017). [https://doi.org/10.1371/journal.pcbi.1005510 Good enough practices in scientific computing]. PLoS Comput Biol 13(6): e1005510.
* Paper: Gentzkow M, Shapiro JM. Code and Data for the Social Sciences: A Practitioner's Guide; 2014. https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf. 
* Video: [https://www.youtube.com/watch?v=4rBX6r5emgQ Reproducible Research: Concepts and Ideas]. Roger Peng. YouTube

'''Slides:'''
* [https://jeremydfoote.com/computational_communication_resources/reproducible_research/lecture/reproducible_research.html Week 2 and 3 slides]
* [https://purdue.brightspace.com/d2l/le/content/798129/viewContent/13239600/View Video of class meeting]


=== Organization ===

Key ideas:
* Folder structure
** Different options, but separate code from data
** Jeremy's approach:
<pre>
my_cool_project
|
|-- README.md # Explanation of project and how to navigate it
|-- Snakefile # Or Makefile - workflow tool
|
|-- data/
|   |-- raw_data/
|   |-- processed_data/
|
|-- code/
|
|-- results/
|   |-- figures/
|
|-- papers/
|
|-- presentations/
</pre>



=== Data Management ===

Key ideas:
* Back up raw data 
* Keep raw data (and make it read-only)
* Step one is to clean the data: create the data you wish you received
** Name variables well
** Use a [https://www.jstatsoft.org/article/view/v059i10 tidy] data structure
* Share data (when possible)

=== Code management ===

Key ideas:
* Version control
* Don't repeat yourself (DRY)
* Build at least a few high-level test cases


== Week 3: Reproducible Research II (May 30) ==


'''Resources:'''
* Blog post: [http://datasci.kitzes.com/lessons/python/reproducible_workflow.html Reproducible Workflows] by Justin Kitzes
* [https://www.youtube.com/watch?v=r9PWnEmz_tc Introduction to Snakemake Tutorial (video)]
* [https://lachlandeer.github.io/snakemake-econ-r-tutorial/index.html An Introduction to Snakemake for social science]
* [https://www.youtube.com/watch?v=zqQM66uAig0 LaTeX introduction (video)]
* [https://www.overleaf.com/learn/latex/Knitr knitr introduction]

'''Slides:'''

* [https://jeremydfoote.com/computational_communication_resources/reproducible_research/lecture/reproducible_research.html Week 2 and 3 slides]
* [https://purdue.brightspace.com/d2l/le/content/798129/viewContent/13254014/View Video of class meeting]


=== Reproducible analyses and papers ===


Key ideas:
* Some big benefits (and some drawbacks) to using text-based tools ([https://bookdown.org/yihui/rmarkdown-cookbook/ Markdown] or [https://www.overleaf.com/ LaTeX])
** Can be put in version control
** Tools like [https://yihui.org/knitr/ knitr] can be used to put code directly into a document
* Make figure creation part of your workflow, have documents point to your figures directory
* Use citation management software that integrates with your document (use [https://www.zotero.org/ Zotero])


=== Sharing ===

Key ideas:
* Share your code and data whenever possible!
* Lots of options - [https://osf.io/ OSF.io], [https://dataverse.harvard.edu/ Harvard Dataverse], etc. 
* Share preprints online



=== Advanced: Workflow Management ===

Key ideas:
* Tools to reproduce as much of the workflow as possible
* README file is much better than nothing
* Even better is a "wrapper" script that runs everything
** Very clear exactly what is run and how
** Some fairly simple options:
*** Python file
*** [https://www.gnu.org/software/make/ GNU Make]
*** [https://snakemake.github.io/ Snakemake]


== Week 4: Computational text analysis: Introduction and Key Concepts (June 6) ==

'''Resources:'''
Text As Data: A New Framework for Machine Learning and the Social Sciences (2022). Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart.

Read chapters 1-7

== Week 5: Computational text analysis: Some "traditional" approaches (June 13) ==

=== Topic modeling ===


=== Embeddings ===

'''Resources:'''
* [https://huggingface.co/blog/getting-started-with-embeddings Getting started with embeddings (Huggingface)]

=== Classification ===

=== Semantic networks ===


* [https://www.youtube.com/playlist?list=PLeo1K3hjS3uuvuAXhYjV2lMEShq2UYSwX NLP Tutorial Playlist Python (YouTube videos)]

== Week 6: Computational text analysis: using LLMs for research (June 20) ==

'''Due:'''
* Final project proposal (details on Brightspace) 

'''Resources:'''

Intro to LLMs:
* [https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5f67941fa59e Mark Riedl. A very gentle introduction to Large Language Models without the hype]
* [https://www.youtube.com/watch?v=bSvTVREwSNw How ChatGPT Works Technically | ChatGPT Architecture (YouTube)]
* [https://amatriain.net/blog/transformer-models-an-introduction-and-catalog-2d1e9039f376/ Transformer Models: An introduction and catalog]
* [https://www.youtube.com/watch?v=iR2O2GPbB0E What are Large Language Models (LLMs)?]

Reflections on LLMs for research:
* [https://arxiv.org/abs/2305.03514 Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2023). Can Large Language Models Transform Computational Social Science?. arXiv preprint arXiv:2305.03514.]

Papers using LLMs:
* [https://arxiv.org/abs/2304.03442 Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. arXiv preprint arXiv:2304.03442.]

== Week 7: Share and discuss works-in-progress (June 27) ==

'''Assignment Due:''' [[Self_Assessment_Reflection | self-assessment reflection]]

'''Topic Presentations:'''
* Juan Pablo (JP) Loaiza-Ramírez
*

'''Work-in-progress Presentations:'''
* Juan Pablo (JP) Loaiza-Ramírez
* Christina Walker

== Week 8: No class - July 4 ==

== Week 9: Share and discuss works-in-progress (July 11) ==

'''Topic Presentations:'''
* Elizabeth Thompson
* Ryan Funkhouser

'''Work-in-progress Presentations:'''
* Dyuti Jha

== Week 10: Share and discuss works-in-progress (July 18) ==

'''Topic Presentations:'''
* Christina Walker
* Hazel Chiu

'''Work-in-progress Presentations:'''
* Elizabeth Thompson

== Week 11: Share and discuss works-in-progress (July 25) ==

Visit from [https://sites.google.com/view/billrand/ Bill Rand], an expert in agent-based moedeling.

'''Topic Presentations:'''
* Dyuti Jha


'''Work-in-progress Presentations:'''
* Hazel Chiu

== Week 12: Share and discuss works-in-progress (August 1) ==

'''Topic Presentations:'''
* Muqing Liu
*

'''Work-in-progress Presentations:'''
* Ryan Funkhouser
* Muqing Liu

'''Assignment Due:'''
* Final project due