TWITTER DATA RADICALIZATION USING SENTIMENT ANALYSIS:

In an age dominated by social media, the internet has become a powerful tool for communication, connection, and unfortunately, radicalization. Among the plethora of platforms available, Twitter stands out as a prominent arena where extremist ideologies find fertile ground to propagate and recruit.

The phenomenon of radicalization, the process by which individuals adopt extreme beliefs and ideologies often leading to violent actions, poses significant challenges to societies worldwide. Understanding how radicalization manifests and spreads on Twitter is crucial for developing effective strategies to counter its influence and mitigate its harmful effects.

This project aims to delve into the complex dynamics of radicalization on Twitter, employing a multi-faceted approach that combines data analysis, machine learning techniques, and social science insights. By examining patterns in user behavior, content dissemination, and network structures, we seek to uncover key insights into the mechanisms driving radicalization online.

Significance:

By shedding light on the mechanisms of radicalization on Twitter, this project contributes to the broader discourse on countering violent extremism and promoting online safety. By understanding the tactics and strategies employed by extremist groups, we can develop more effective measures to prevent radicalization, protect vulnerable individuals, and foster healthier online communities.

Overview

This project aims to analyze Twitter data to identify patterns of radicalization.

Features

Data Collection:

Script to collect Twitter data using the Twitter API.

Analysis:

Tools for analyzing tweet content and user behaviors.

Visualization:

Graphical representation of data insights.

Requirements

Python 3 Dependencies listed in requirements.txt

Installation

Clone the repository:

bash Copy code git clone [https://github.com/Vivekroy286/Twitter_data_radicalization.git]

Install dependencies:

Copy code pip install -r requirements.txt

Usage

Data Collection:

Modify config.py with your Twitter API credentials.

Run python collect_data.py to start collecting Twitter data.

Analysis:

Use analyze.py to perform analysis on collected data.

Visualization:

Run visualize.py to generate visualizations based on analysis results.

Contributing

Feel free to contribute to this project. Here are a few ways you can help:

Improve data collection efficiency. Enhance analysis algorithms. Add more visualization options.

Acknowledgments

Hat tip to anyone whose code was used. Inspiration. etc.