Background papers on analysing society with computational methods:
- Grimmer, Justin, and Brandon M. Stewart. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis (2013): mps028.
- D., Pentland, A.S., Adamic, L., Aral, S., Barabasi, A.L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M. and Jebara, T., 2009. Life in the network: the coming age of computational social science. Science (New York, NY), 323(5915).
Two references for the techniques we are using. The first one is a good read if you are interested in how social scientists think about web scraping as a method. The second one is a now a bit outdated references on EDA in Python but still a classic and worth a read.
- Marres, N. and Weltevrede, E., 2013. Scraping the social? Issues in live social research. Journal of Cultural Economy, 6(3), pp.313-335.
- Wes McKinney, 2013, Data Analysis in Python.
- Ray Johns, Political Python Scraping Congressional documents with Scrapy, https://towardsdatascience.com/political-python-1e8eb46c1bc1
- Sara Robinson, 2018, Classifying congressional bills with machine learning, https://medium.com/@srobtweets/classifying-congressional-bills-with-machine-learning-d6d769d818fd
- Suresh Kumar Mukhiya, Usman Ahmed, 2020, Hands-On Exploratory Data Analysis with Python, https://www.packtpub.com/product/hands-on-exploratory-data-analysis-with-python/9781789537253
- Python EDA for NLP problems: https://www.kaggle.com/aceconhielo/nlp-process-explanation-top-28-solution
- Python EDA for Regression problems: https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python
- Python EDA for Titanic problem: https://www.kaggle.com/mrisdal/exploring-survival-on-the-titanic
- Smith, M.A., Rainie, L., Shneiderman, B. and Himelboim, I., 2014. Mapping Twitter topic networks: From polarized crowds to community clusters. Pew Research Center, 20. http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
- Jain, A.K., 2010. Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), pp.651-666.
- Paoline III, E.A. and Terrill, W., 2005. The impact of police culture on traffic stop searches: an analysis of attitudes and behavior. Policing: An International Journal of Police Strategies & Management, 28(3), pp.455-472.