Building a Media Ecosystem Observatory from Scratch: Infrastructure, Methodology, and Insights

TL;DR

This paper introduces the Canadian Media Ecosystem Observatory, a scalable infrastructure for monitoring and analyzing political and media discourse across multiple digital platforms in near real time.

Contribution

It presents a comprehensive infrastructure with custom crawlers, data normalization, and semantic analysis tools, providing a new model for digital trace research in media ecosystems.

Findings

Demonstrated utility through analysis of Canadian political events

Provided real-time insights into media discourse dynamics

Enabled advanced similarity search and topic modeling

Abstract

Understanding the flow of information across today's fragmented digital media landscape requires scalable, cross-platform infrastructure. In this paper, we present the Canadian Media Ecosystem Observatory, a national-scale infrastructure designed to monitor political and media discourse across platforms in near real time. Media Ecosystem Observatory (MEO) data infrastructure features custom crawlers for major platforms, a unified indexing pipeline, and a normalization layer that harmonizes heterogeneous schemas into a common data model. Semantic embeddings are computed for each post to enable similarity search and vector-based analyses such as topic modeling and clustering. Processed and raw data are made accessible through API, dashboards and website, supporting both automated and ad hoc research workflows. We illustrate the utility of the observatory through example analyses of major Canadian political events, including Meta's 2023 news ban and the recent federal elections. As a whole, the system offers a model for digital trace infrastructure and an evolving research platform for studying the dynamics of modern media ecosystems.