Classifying Newspaper Articles for Wildlife Value Orientation :

Objectives:

Classify a corpus of grizzly bear articles for wildlife value orientations (WVO) in order to:

• determine if there is a shift in WVO framing similar to Manfredo et al., 2021
• determine if external pressures impact those trends such as
  • the type of newspaper coverage (local and in bear range, local and not in bear range, "elite" national)
  • ownership of newspaper (conglomerate or not)
  • topic (bear attack or not)
  • time (year, months or years before or after a specific event)

Methodology:

• Supervised classification. Final algorithm to be determined.
• Some sort of multilevel or multivariate glm

Anticipated Results:

• Time series showing the % mutual newspapers through year for the different newspaper types

Installation Instructions:

Running this code requires the following libraries.

Usage Instructions:

Note: These usage instructions are for testing. This project is in progress, so there are still many testing scripts and few final scripts.

In this project there are three script directories:

1. scripts/final
2. scripts/test
3. quarto_docs

Within the scripts/final directory are three (mostly) final scripts for

1. downloading the articles that were coded in the VIP class 00_article_download.R
2. creating the dataframe with the article text, the codes, and other variables 01_initial_create_article_dataframe.R
3. some text cleanup 02_text_preprocessing.R. There is more text preprocessing in the model_training_tuning.qmd quarto doc

Within the scripts/test directory are many testing and scratch scripts. The only scripts that may be of interest are:

1. 03_classification.R the initial model training. However, using this script I could not actually use the trained models to predict unlabeled data. I then switched to using textrecipes.
2. Scripts starting with _tidymodels_*.R are the scripts I used to test out the individual classification models using tidymodels and textrecipes. Once these were tested, I transferred the code in the model_training_tuning.qmd quarto doc.
3. _grizzly_article_classification_tdm.R is the script with a copy of the code used in the TDM studio to classify the grizzly bear corpus using the trained models. This code will only work in our group TDM workbench
4. _grizzly_preds_local_national_edu.R Within this script I attempt some visualizations with the resulting WVO predictions on an early version of the corpus using the models trained in the quarto doc. This code is not well commented, but I will continue to update comments and code in this script.
   • The data files grizzly_bear_05_preds_gamma.csv contain the article ID, Title, Date, Total_Gamma, reg_05_pred_class, knn_05_pred_class, and rf_05_pred_class. This file was downloaded from the TDM studio where the actualy prediction had to take place.

Note: the 05 in the file and variable name corresponds to a gamma threshold. The gamma value represents the... The 05 is a 0.5 gamma threshold, 055 is a 0.55 gamma threshold, etc.

Within the home project directory is the quarto_docs folder. This contains the model_training_tuning.qmd file. This is where three different supervised classification models were trained:

1. regression
2. k nearest neighbors
3. random forest

The trained models were saved as RDS files and uploaded to the TDM server. These models were then used to predict the values of a corpus of grizzly bear articles. See the _grizzly_article_classification_tdm.R in the scripts/test directory. These predictions were saved as the grizzly_bear_0*_preds_gamma.csv files and downloaded from TDM.

Contributing:

• If you are open to contributions, provide guidelines for contributing to the project.

License:

• Creative Commons Zero v1.0 Universal

Contact Information:

• For questions related to the code please email Katie Murenbeeld at [email protected]