Misleading Ourselves: How Disinformation Manipulates Sensemaking
October 18, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Stephen Prochaska, Julie Vera, Douglas Lew Tan, Kate Starbird
arXiv ID
2410.14858
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY,
cs.SI
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Informal sensemaking surrounding U.S. election processes has been fraught in recent years, due to the inherent uncertainty of elections, the complexity of election processes in the U.S., and to disinformation. Based on insights from qualitative analysis of election rumors spreading online in 2020 and 2022, we introduce the concept of manipulated sensemaking to describe how disinformation functions by disrupting online audiences ability to make sense of novel, uncertain, or ambiguous information. We describe how at the core of this disruption is the ability for disinformation to shape broad, underlying stories called deep stories which determine the frames we use to make sense of this novel information. Additionally, we explain how sensemakings orientation around plausible explanations over accurate explanations makes it vulnerable to manipulation. Lastly, we demonstrate how disinformed deep stories shape sensemaking not just for a single event, but for many events in the future.
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