Concept: Create a web page or application that aggregates news articles based on user-defined keywords, then quantifies each article’s political leaning (left, center, right) and provides other indices like “Event Likelihood,” “Relevance,” and “Relatedness.”
Goal: Help users quickly assess differing viewpoints and gauge how important or credible a story might be across multiple political and analytical dimensions.
A search bar where users enter keywords.
A results page listing articles relevant to those keywords. An article detail view showing “Left/Right Leaning Index,” “Event Likelihood,” and optional advanced metrics. Data Sources:
News APIs or RSS feeds (e.g., Google News, Ground News, or other aggregators).
A classification model (ML or heuristic-based) for political leaning.
Additional scoring logic for relevance and likelihood of events.
Front End: Browser-based UI or downloadable app built with HTML/CSS/JavaScript (or a framework like React). Back End: Python/Node.js server that fetches articles, runs classification and scoring, then returns summarized data to the client. Database: Stores article metadata, user interactions, or advanced analytics for faster retrieval.
Write a small script (Python, Node.js, etc.) to query external APIs (e.g., NewsAPI, RSS feeds). Parse incoming JSON/XML results and store them temporarily in memory or a database. Classification and Scoring
Political Leaning: Use a basic NLP model (e.g., a Python scikit-learn classifier) trained on known left/right sample texts, or heuristics based on recognized sources. Event Likelihood: Simple keyword analysis (e.g., “confirmed,” “official,” “rumor,” “alleged”) or advanced models that assign probabilities. Relevance/Importance: Tally occurrences of user keywords, plus article recency, source credibility, and clickthrough rate. Relatedness (optional): For advanced features, compute article-to-article similarity with vector embeddings (e.g., using spaCy or Sentence Transformers). Summary Generation: Summarize each article and group them by category (left, center, right). A library like spaCy or gensim can be used, or even OpenAI GPT-based summarization if feasible.
Front-End Display
Build a basic HTML page (or React app) that lists articles with their indices. When an article is clicked, show more detailed data (e.g., Leaning, Event Likelihood, Relevance Score). Optional: E-Commerce / SSH Shop
Integrate a basic store or donation system if you want to monetize or collect support for the project. Could be implemented via a simple PayPal or Stripe integration.
Impact:
Empowers users to see multiple viewpoints, reducing echo chambers. Offers quick, data-driven insights into political biases, event probability, and article importance. Encourages more nuanced understanding and discussion around current events.
Future Directions:
Incorporate user feedback loops, letting users vote on perceived bias or correctness. Automate advanced AI-based summarization for global issues, grouping them into one “master summary.” Expand to additional metrics (e.g., sentiment analysis, ethical rating, or corporate ownership transparency). Integrate crowd-sourced fact-checking to further enhance the reliability of displayed articles.