Improving DisInfoVis

Designing Effective Network visualization Representations of Disinformation Operations

Supporting Materials for EuroVIS 2020 poster …


 
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Fast Forward Video

Abstract

Poster


Phase 1 : DisInfoVis Recommendations Study

Qualitative evaluation of static and dynamic representations of temporal network visualizations.

Participants were provided with introductory learning materials (static and dynamic), as well as static and dynamic representations of three state-backed disinformation operations, as shown.

INTRODUCTION

Introduction to network visualizations - materials used in training participants.

 

VENEZUELA

Venezuelan Network Visualization over time - Activity between 2016 and 2019, English language tweets.

Video:  https://www.youtube.com/watch?v=cvIqkNWi5tc

Venezuelan Network Visualization - example stimulus used for dynamic condition (one of three videos).

 

Slideshow : https://j.mp/disInfoVis_EVVenezuela

Venezuelan Network Visualization - example stimulus used for static condition (PDF of one of three slideshows)

 
 

CHINA

Chinese Network Visualization over time - Activity between 2008 and 2019, English language tweets

Video : https://www.youtube.com/watch?v=kCWrLwX73GY

Chinese Network Visualization - example stimulus used for dynamic condition (one of three videos).

Slideshow : https://j.mp/disInfoVis_EVChina

Chinese Network Visualization - example stimulus used for static condition (PDF of one of three slideshows)

 
 

RUSSIA

Russian Network Visualization over time - Activity between 2014 and 2019, English language tweets from the Internet Research Agency, according to Twitter.


Phase 2 : DisInfoVis Representations for Reporting

Six temporal network visualizations of disinformation operations used in state-backed information operations on Twitter, and how they evolved over the last decade.

These graphics and six accompanying videos of the disinformation operations are presented in the following Medium article:

This work was funded by the Mozilla Foundation through Open Source Support Award MF-1909-07167.


Recommendations

Our recommendations for DisInfoVis are captured in this wireframe mock-up :


ACKNOWLEDGMENT

This work was part funded by an Open Source Support Award from the Mozilla Foundation (MF-1909-07167) and a project stipend from the Data Science Institute, City, University of London.


06/04/20