TEVISE: An Interactive Visual Analytics Tool to Explore Evolution of Keywords' Relations in Tweet Data

TL;DR

TEVisE is a visual analytics tool that enhances exploration of keyword relations in tweet data over time, improving usability and understanding of evolving trends through an improved adjacency matrix visualization.

Contribution

The paper introduces TEVisE, a novel interactive tool with an enhanced adjacency matrix for visualizing keyword evolution in tweets, addressing clutter issues in previous tools.

Findings

TEVisE improves accuracy in relation exploration tasks.

Users prefer the combined summary and timeline views.

TEVisE reduces cognitive load compared to previous tools.

Abstract

Recently, a new window to explore tweet data has been opened in TExVis tool through visualizing the relations between the frequent keywords. However, timeline exploration of tweet data, not present in TExVis, could play a critical factor in understanding the changes in people's feedback and reaction over time. Targeting this, we present our visual analytics tool, called TEVisE. It uses an enhanced adjacency matrix diagram to overcome the cluttering problem in TExVis and visualizes the evolution of frequent keywords and the relations between these keywords over time. We conducted two user studies to find answers of our two formulated research questions. In the first user study, we focused on evaluating the used visualization layouts in both tools from the perspectives of common usability metrics and cognitive load theory. We found better accuracy in our TEVisE tool for tasks related to reading exploring relations between frequent keywords. In the second study, we collected users' feedback towards exploring the summary view and the new timeline evolution view inside TEVisE. In the second study, we collected users' feedback towards exploring the summary view and the new timeline evolution view inside TEVisE. We found that participants preferred both view, one to get overall glance while the other to get the trends changes over time.