On Understanding Misleading Visualizations, Automatic Detection, And Prevention

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "On Understanding Misleading Visualizations, Automatic Detection, And 
Prevention"

By

Mr. Yu Ho LO


Abstract:

Visualizations such as charts and graphs are used as rhetorical devices in 
news, blogs, and social media to support and further strengthen the message 
conveyed to the audience. Research shows the persuasive power of visualization. 
With equivalent information, the presence of a visualization--compared to a 
table--has a significant effect on changing or reinforcing the reader's 
attitude toward the concerned matter. Misinformation on the Internet is a 
significant challenge to societies that rely heavily on digital information. 
Increasingly, charts are included in the dissemination of misinformation. While 
the study of misinformation on the Internet often focuses on textual content, 
it rarely addresses the associated visual graphics.

This thesis examines the current state of the art in the automatic detection 
and prevention of misleading visualizations. The initial efforts in this field 
involved collecting misleading visualizations to confirm their existence. 
Researchers then conducted perceptual experiments to verify the misleading 
effects on human perception and reasoning. Automatic detection of misleading 
visualizations is a recent development in visualization research.

This research explores the landscape of misleading visualizations by collecting 
over one thousand real-world examples and identifying 74 types of issues, 
forming a taxonomy of 12 categories. It also investigates the use of multimodal 
Large Language Models (LLMs) for detecting misleading visualizations, 
demonstrating their potential in analyzing complex charts and enhancing 
visualization literacy. Additionally, this work develops effective explanation 
methods, including Bret Victor's concept of Explorable Explanations, to educate 
audiences and guide authors in modifying misleading visualizations.

Building on the groundwork of understanding, recent breakthroughs in detection 
methods make it possible to develop prevention mechanisms such as automatic 
correction suggestions and in-situ annotations. However, further development of 
this research direction requires benchmarks to compare the performance of 
different detection and correction methods.


Date:                   Monday, 19 August 2024

Time:                   10:00am - 12:00noon

Venue:                  Room 3494
                        Lifts 25/26

Chairman:               Prof. Jidong ZHAO (CIVL)

Committee Members:      Prof. Huamin QU (Supervisor)
                        Prof. Chiew-Lan TAI
                        Dr. Shuai WANG
                        Prof. Hongbo FU (AMC)
                        Prof. Kwan-Liu MA (UC Davis)