IDEIA: A Generative AI-Based System for Real-Time Editorial Ideation in Digital Journalism

TL;DR

This paper introduces IDEIA, a generative AI system that enhances real-time editorial ideation in digital journalism by integrating trend analysis and automated content suggestions, significantly improving efficiency.

Contribution

The paper presents a novel modular AI system that combines trend monitoring and content generation for journalism, demonstrating practical benefits and discussing ethical considerations.

Findings

Up to 70% reduction in ideation time

Improved editorial productivity and quality

Scalable architecture for newsroom integration

Abstract

This paper presents IDEIA (Intelligent Engine for Editorial Ideation and Assistance), a generative AI-powered system designed to optimize the journalistic ideation process by combining real-time trend analysis with automated content suggestion. Developed in collaboration with the Sistema Jornal do Commercio de Comunica\c{c}\~ao (SJCC), the largest media conglomerate in Brazil's North and Northeast regions, IDEIA integrates the Google Trends API for data-driven topic monitoring and the Google Gemini API for the generation of context-aware headlines and summaries. The system adopts a modular architecture based on Node.js, React, and PostgreSQL, supported by Docker containerization and a CI/CD pipeline using GitHub Actions and Vercel. Empirical results demonstrate a significant reduction in the time and cognitive effort required for editorial planning, with reported gains of up to 70\% in the content ideation stage. This work contributes to the field of computational journalism by showcasing how intelligent automation can enhance productivity while maintaining editorial quality. It also discusses the technical and ethical implications of incorporating generative models into newsroom workflows, highlighting scalability and future applicability across sectors beyond journalism.