The Anatomy of a Modular System for Media Content Analysis

TL;DR

This paper presents a modular AI system for media content analysis that integrates multiple AI components to automate complex social science research tasks, enabling diverse applications like media comparison and public mood monitoring.

Contribution

It introduces a flexible, modular framework that combines various AI modules for media content analysis, facilitating complex, cooperative tasks without central coordination.

Findings

Enabled large-scale media content studies across countries.

Supported modeling of user preferences and public mood.

Demonstrated flexible integration of AI modules for social science research.

Abstract

Intelligent systems for the annotation of media content are increasingly being used for the automation of parts of social science research. In this domain the problem of integrating various Artificial Intelligence (AI) algorithms into a single intelligent system arises spontaneously. As part of our ongoing effort in automating media content analysis for the social sciences, we have built a modular system by combining multiple AI modules into a flexible framework in which they can cooperate in complex tasks. Our system combines data gathering, machine translation, topic classification, extraction and annotation of entities and social networks, as well as many other tasks that have been perfected over the past years of AI research. Over the last few years, it has allowed us to realise a series of scientific studies over a vast range of applications including comparative studies between news outlets and media content in different countries, modelling of user preferences, and monitoring public mood. The framework is flexible and allows the design and implementation of modular agents, where simple modules cooperate in the annotation of a large dataset without central coordination.