This code accompanies the pre-print "The New York Times Distorts the Palestinian Struggle: A Case Study of Anti-Palestinian Bias in American News Coverage of the First and Second Palestinian Intifadas", recently submitted to the Journal of Palestine Studies. The study proves a history of bias against Palestine in the New York Times during the First and Second Palestinian Intifadas.

This codebase is run on an archived version of the concretely-annotated New York Times, documented at this reference:

Francis Ferraro, Max Thomas, Matthew Gormley, Travis Wolfe, Craig Harman, and Benjamin Van Durme. "Concretely Annotated Corpora." In The Proceedings of the NIPS Workshop on Automated Knowledge Base Construction (AKBC). NIPS Workshop 2014.

My content analysis was performed in python 3.8 on a 16-core Ubuntu 18.04 machine

Clone the repo and submodules

git clone --recurse-submodules https://github.com/hollyjackson/NYT_Content_Analysis.git

This codebase requires a number of natural language processing libraries:

• spaCy
• NLTK
• scikit-learn
• Keras

These, along with other required libraries, can all be installed as follows:

pip3 install -r requirements.txt

This script preprocesses the Concretely Annotated New York Times and parses articles related to Israel and/or Palestine in a specific year of a specific Intifada. Before running this script, sort and extract all year-month tar.gz files into two folders, based on the dates of each Intifada.

The script can be run as follows with two optional command-line arguments:

python3 preprocessing.py --intifada {'first_intifada' or 'second_intifada'} --verbose {True or False}

The defaults are 'first_intifada' and False.

This script processes articles related to Israel and/or Palestine in the Concretely Annotated New York Times and classifies the voice (active or passive) of all sentences with Israeli or Palestinian subjects. It should be run after preprocessing.py (all pre-processed data must be stored in a subdirectory called sorted_files).

The script can be run as follows with two required command-line arguments:

python3 voice_identifier.py --intifada {'first_intifada' or 'second_intifada'} --year {must be a valid year in the respective Intifada}

This can easily be parallelized using a bash scrhipt.

This script instances of passive and active voice in articles processed by my NLP pipeline. It should be run after preprocessing.py and voice_identifier.py.

This script provides a user interface to generate a training set from a sample of 500 random articles on Palestine and/or Israel from the New York Times during the First and Second Intifadas. It should be run after preprocessing.py. When run, the user will be prompted to blindly tag the violent sentiment of the set of words appearing in this sample. The results will be saved to training_data.csv.

This script counts references to violence in articles processed by my NLP pipeline. It should be run after preprocessing.py and generate_training_set.py.

I have not included any plotting scripts since they were specific to the the data I mined and the topics I focused on. There are many equally successful and useful ways to plot the data, and those should be explored by the researcher.