terrordetect

Identifying potential criminals and terrorists from social media analysis.

CPSC 571 Research Project

• The project report is available here.

Requirements

• Latest version of Python
  • NB: Make sure that pip is also installed alongside the Python interpreter.
• ( ❗ Optional ❗ ) Git.
  • NB: Refer to the next section for details.

Installation and usage

1. Either clone the repository or download it from the main branch as a ZIP file.
   • NB: If going with the former method, Git is required.
   • 
2. Unzip and extract the ZIP file to a folder somewhere in your local drive.
   • If cloned, skip this step.
3. Navigate to the extracted/cloned folder.
   • Unless changed, it should be 'TerrorDetect'
4. Open a terminal in this new folder, type and run the following command:

    $ pip install -r requirements.txt

Quick rundown of files included

• combiner.py: Utility module for combining all the gathered .csv files into a singular dataset.
• datasetcreator.py: Module for generating individual datasets consisting of 10 tweets per keyword.
  • as defined in the program as a keyword of commonly used extermists.
• naivebayes: Module which uses Multinomial Naive Bayes and Logistic Regression classification models.
  • Also performs tweet cleanup and preprocessing as well as visualizes the results.
    • confustion matrix and accuracy prediction scores
• rforest.py: Same as above but uses the Random Forest Classification model instead.
• svm.py: Uses Simple Vector Classification with the same method of generating the results and visualization.