Διαδικτυακή εφαρμογή εμφάνισης και αξιολόγησης ειδήσεων με χρήση Angular και Python
Web App platform for reading and reviewing news using Angular and Python
View/
Abstract
In today's world, information is produced and consumed at an unprecedented speed through the internet. News websites generate hundreds, and in some cases, thousands of articles daily. The accuracy of the content posted daily remains largely unchecked, both by any authority and in any way, resulting in cases where news is disseminated, sometimes based on incorrect information from a report, sometimes disguised as humorous, and sometimes to serve specific interests. There is no protection measure for the information consumed by the reader, and whether something is believed, if it is false, depends on the individual's education, which can sometimes be dangerous.
This project aims to create a web application where articles are gathered from various news agencies on the internet, stored, and displayed on a page of the application. Users could evaluate the news they read, and this evaluation is visible to all users. The application can also evaluate the evaluations to some extent, based on certain criteria that are gathered during the use of the application, and then accept or reject each evaluation. The criteria implemented include the location of the news, the user's location through geolocation, the time it took the user to read the text, and whether they reached the end of the text. Based on user actions, the application can add or remove points and privileges from users. This way, each user creates a reputation that gives different weight to their evaluations each time they evaluate a new article. The idea of rewarding users is based on the concept of the Stack Exchange network, where users are evaluated based on the answers, they provide to questions from other users on various topics such as programming on Stack Overflow, photography on Photography, and many other communities.
For the implementation of the platform, Python 3.7 and the FastAPI framework were used for the back-end API and the scrap engine. TypeScript and the Angular 14 framework were used for the front-end. Finally, the SQLite database was used for data storage.