Agent-based visualization of streaming text

TL;DR

This paper introduces an agent-based visualization system for streaming text data, where agents representing words dynamically organize based on co-occurrence, revealing primary topics through evolving clusters.

Contribution

It presents a novel agent-based visualization infrastructure that dynamically maps streaming text data into visual clusters based on word co-occurrence.

Findings

Visualizes streaming text with stable, dynamic agent layouts.

Clusters primary topics effectively in real-time.

Uses co-occurrence to inform agent positioning and sizing.

Abstract

We present a visualization infrastructure that maps data elements to agents, which have behaviors parameterized by those elements. Dynamic visualizations emerge as the agents change position, alter appearance and respond to one other. Agents move to minimize the difference between displayed agent-to-agent distances, and an input matrix of ideal distances. Our current application is visualization of streaming text. Each agent represents a significant word, visualizing it by displaying the word itself, centered in a circle sized by the frequency of word occurrence. We derive the ideal distance matrix from word cooccurrence, mapping higher co-occurrence to lower distance. To depict co-occurrence in its textual context, the ratio of intersection to circle area approximates the ratio of word co-occurrence to frequency. A networked backend process gathers articles from news feeds, blogs, Digg or Twitter, exploiting online search APIs to focus on user-chosen topics. Resulting visuals reveal the primary topics in text streams as clusters, with agent-based layout moving without instability as data streams change dynamically.