True or false? Cognitive load when reading COVID-19 news headlines: an eye-tracking study
February 16, 2023 Β· Declared Dead Β· π Conference on Human Information Interaction and Retrieval
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Li Shi, Nilavra Bhattacharya, Anubrata Das, Jacek Gwizdka
arXiv ID
2302.08597
Category
cs.HC: Human-Computer Interaction
Citations
4
Venue
Conference on Human Information Interaction and Retrieval
Last Checked
4 months ago
Abstract
Misinformation is an important topic in the Information Retrieval (IR) context and has implications for both system-centered and user-centered IR. While it has been established that the performance in discerning misinformation is affected by a person's cognitive load, the variation in cognitive load in judging the veracity of news is less understood. To understand the variation in cognitive load imposed by reading news headlines related to COVID-19 claims, within the context of a fact-checking system, we conducted a within-subject, lab-based, quasi-experiment (N=40) with eye-tracking. Our results suggest that examining true claims imposed a higher cognitive load on participants when news headlines provided incorrect evidence for a claim and were inconsistent with the person's prior beliefs. In contrast, checking false claims imposed a higher cognitive load when the news headlines provided correct evidence for a claim and were consistent with the participants' prior beliefs. However, changing beliefs after examining a claim did not have a significant relationship with cognitive load while reading the news headlines. The results illustrate that reading news headlines related to true and false claims in the fact-checking context impose different levels of cognitive load. Our findings suggest that user engagement with tools for discerning misinformation needs to account for the possible variation in the mental effort involved in different information contexts.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted