"I don't trust them": Exploring Perceptions of Fact-checking Entities for Flagging Online Misinformation
October 01, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hana Habib, Sara Elsharawy, Rifat Rahman
arXiv ID
2410.00866
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY,
cs.SI
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The spread of misinformation through online social media platforms has had substantial societal consequences. As a result, platforms have introduced measures to alert users of news content that may be misleading or contain inaccuracies as a means to discourage them from sharing it. These interventions sometimes cite external sources, such as fact-checking organizations and news outlets, for providing assessments related to the accuracy of the content. However, it is unclear whether users trust the assessments provided by these entities and whether perceptions vary across different topics of news. We conducted an online study with 655 US participants to explore user perceptions of eight categories of fact-checking entities across two misinformation topics, as well as factors that may impact users' perceptions. We found that participants' opinions regarding the trustworthiness and bias of the entities varied greatly, aligning largely with their political preference. However, just the presence of a fact-checking label appeared to discourage participants from sharing the headlines studied. Our results hint at the need for further exploring fact-checking entities that may be perceived as neutral, as well as the potential for incorporating multiple assessments in such labels.
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