Simon Injiri
Brian Wandera
Charles Njenga
Catherine Wanjiru
The Project consists of a machine learning model that is the backend of our project. The ML model tests and trains it's data using the WebHouse Dataset which we implemented as an API used in the Android app and the front end web plugin.
The output displays a percentage confidence of real or fake, similarity of text to title, and polarity of the text.
The STL API queries the Tallos Intelligence Resources and the Public API for Apility.io for much of the analytics in regards to IP and Domain Checker. These three checks -IP address blacklist, Domain blacklist and IP address historical blacklist- are summarized and returned as a global score for the IP address.
This API call also returns detailed information about the IP address from different sources:
- when the domain was registered and the registrar
- IP Geolocation
- AS information
- WHOIS informaton
- Blacklists where the IP address was found (if any).
- Blacklists where the Hostname was found (if any).
The project does not require a user to download any machine learning library but only download the apk of the application from this site https://drive.google.com/file/d/1nAvX97uhTnjMkT_q4RkZ4-tbi_gMBIsv/view?usp=sharing. After installing the android application, the user will be able to paste the urls into the application and it is parsed in the API of the machine Learning model
It works on the same procedure as the android app but by having a Web Extension being installed in the browser which extacts the webpage being browsed and parses the data using the Machine Learning model API.
The See The Light API uses part of the Machine Learning Model developed by @bmbejcek. The See The Light app goes ahead and adds dns credibility assessment to check on the source of the story. If such a site is associated with fake news, then it reduces the credibility of such a site.
The API endpoint to check the authenticity of a news article is: Click Here To View A Demo of the API