Vizible seeks to extract insights from online news media by understanding the underlying language, quantifying it, and visualizing it. My challenge was to present this complex collection of data and relationships in a way that people could not just make sense of it, but that they could use it to find insights and navigate to the desired content. Achieving this goal required the design of several user portals and the data visualizations themselves.

Scenario: as a marketer of electric vehicles, I want to understand how the language in my upcoming press release might be received by the news media covering that topic. Vizible's platform gathers the news media –digesting each article– and then performs a sentence-to-sentence language comparison between the press release and the news media.

The visualization shows how the press release relates to the entire discussion on electric vehicles, the kind of language the publishers use, and the extent to which publishers might quote (vs. loosely paraphrase) the press release.

Scenario: as a researcher in the world of electric vehicles, I want to understand the conversation quantitatively. Vizible's platform gathers news media on the topic, uses machine learning to understand each document, analyze the publisher and build a mesh of previously unseen metadata around the conversation.

This visualization allows the user to stack, re-stack and shuffle these dimensions to find interesting data stories. A researcher might discover that when top-tier publishers talk about Rivian in a positive way, it's only when the story accompanies mentions of Elon Musk.