What is it?

Tweetoxicity:2.0 is a program that analyses Twitter user behavior through their actions on their Twitter Profile. The program utilizes machine learning to give Twitter users appropriate score according to their tweets or retweets. This program is meant for educational purposes and no ill intentions exists prior to creating this program. This program was an update from my old project called TweeToxicity.

How does it work?

The program also utilizes Selenium and BeautifulSoup to scrap data from Nitter.

The scrapped data is then sent to the program which keeps the machine learning model, and asks the program politely to assess the given user with its already made fined-tuned pre-trained transformers model. The result, having the shape of a String data is then sent back to UI to be outputted as a readable human language to the website.

How to Start the Web App

1. Make sure you have Docker installed on your system.

2. Navigate to the project directory in your terminal.

3. Run the following command:

   docker-compose up --build

   This command will build the Docker images and start the containers defined in the docker-compose.yml file.

4. Once the containers are up and running, you should be able to access the web app through your browser at the localhost:8080 address.

5. To stop the app, you can use Ctrl+C in the terminal where docker-compose is running, or run docker-compose down in a new terminal window from the project directory.

Frequently Asked Question

Q : "This program sounds controversial"
A : Yes. We couldn't hide the fact that making such a program will spawn controversial discussions about the true nature of this program, however, we are only interested in the use of Flask as an integral part of nowadays web applications. The idea behind the program itself is derived from other popular Twitter user analytic services, we just made them open-source and free.

Q : "What if I got a bad score?"
A : Getting a bad score does not mean you are a bad person. No, definitely not. TweetToxicity is made to calculate how "unfiltered" you are at using the internet. We all know the internet is a place to freely express ourselves without needing many "masks" to be likable by other people. The program could not (and will never) asses someone's personality based on Twitter alone. The program does not know if you use curse words to express excitement, happiness, surprise, or actual ill intent.

Q : "What is the purpose of this program?"
A : Simple, as a submission for our end-of-semester exam. Other than that, we would have never made this out of pure interest or curiosity.

Q : "I acted well in my whole Twitter journey, yet I got a bad score?"
A : Because the program is not perfect. Machine learning technology is so hard to be evolved, as it is extremely data-driven. While there are roughly 44 Zettabyte of data in 2020 alone, does not mean we can use all the data for this project. Data needs to be picked, cleaned, processed, and finally implemented so we can achieve a good AI Model.

Q : "Can I use this as proof of someone's bad behavior in real life?"
A : What? No. Even if you had to use this and give your ex a piece of your mind, no. We do not condone any form of harassment, bullying, or even threats following the use of the app. The use of this app is for educational and entertainment purposes only. Despite the app name, the use of this app for self-evaluation or self-reflection (and the not so "self" counterpart) is NOT RECOMMENDED. There are more things to be considered for machines to assess human personality.

Q : "The whole project could easily be done using X module, and Z method. This is just a waste of time"
A : That's not a question. For critics, advice, and things you'd say to the developer, please use GitHub discussion function.

End-User License Agreement

As per the MIT License:

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

1. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

2. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Contributing

Pull requests are welcome. For major changes, how-to, and in-depth explanations, please contact one of the authors.

License

This project is licensed under MIT License - see the LICENSE file for details