https://github.com/bhodrolok/terrordetect

Identifying potential criminals and terrorists from social media analysis.
https://github.com/bhodrolok/terrordetect

Last synced: 3 months ago
JSON representation

Identifying potential criminals and terrorists from social media analysis.

• Host: GitHub
• URL: https://github.com/bhodrolok/terrordetect
• Owner: bhodrolok
• Created: 2021-12-14T08:00:25.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-06-21T05:24:47.000Z (7 months ago)
• Last Synced: 2024-09-17T08:04:40.918Z (4 months ago)
• Language: Python
• Size: 357 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# terrordetect

Identifying potential criminals and terrorists from social  media analysis.

#### CPSC 571 Research Project

- The project report is available [here](https://github.com/Bhodrolok/TerrorDetect/blob/main/CPSC571-ProjRep-G20.pdf).

### Requirements

- Latest version of [Python](https://www.python.org/downloads/)

    - NB: Make sure that `pip` is also installed alongside the Python interpreter.

- ( :heavy_exclamation_mark: Optional :heavy_exclamation_mark: ) [Git](https://www.git-scm.com/downloads).

    - NB: Refer to the next section for details.

### Installation and usage

1. Either clone the [repository](https://github.com/bhodrolok/TerrorDetect.git) or download it from the main branch as a ZIP file.

    - NB: If going with the former method, [Git](https://www.git-scm.com/downloads) is required.

    - ![image](https://github.com/bhodrolok/TerrorDetect/assets/51386657/294342b0-590f-49d2-95df-af56e472fb7c)

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'

5. Open a terminal in this new folder, type and run the following command:

   ```console

    $ pip install -r requirements.txt

   ``` 

### Quick rundown of files included

- [combiner.py](./combiner.py): Utility module for combining all the gathered `.csv` files into a singular dataset. 

- [datasetcreator.py](./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](./naivebayes.py): Module which uses [Multinomial Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes) and [Logistic Regression](https://en.wikipedia.org/wiki/Logistic_regression) classification models.

    - Also performs tweet cleanup and preprocessing as well as visualizes the results.

        - confustion matrix and accuracy prediction scores

- [rforest.py](./rforest.py): Same as above but uses the [Random Forest](https://en.wikipedia.org/wiki/Random_forest) Classification model instead.

- [svm.py](./svm.py): Uses [Simple Vector Classification](https://en.wikipedia.org/wiki/Support_vector_machine) with the same method of generating the results and visualization.