This project was my first project involving machine learning and Twitter's API. I used a Passive Aggressive Classifier, utilizing over 25,000 sample news articles to create the model. I then used the tweepy wrapper for the Twitter Developer API to integrate it.
This program is designed so that when you mention the bot, such as doing "@fakenewschecker" (the handle you mention will depend on your bot's Twitter handle), and then also type the link, within 10 seconds the bot will reply with a conclusion of whether the model determines the article is real or fake. I will upload a list soon of supported news websites and unsupported websites.
Go the keys.txt file and change the keys according to what the text file says. For example, the first line is CONSUMER_KEY, second is CONSUMER_SECRET, etc. Do not put spaces or anything else on the lines except for your respective token/key/secret text.
This program was coded in Python 3.8, so you will have to do the following commands to install the packages:
pip install tweepy
pip install requests
pip install pandas
pip install sklearn
pip install urllib3
pip install beautifulsoup4
Note: you may have to do pip3 install instead of pip install for installing packages depending on your OS, comptuer, etc