Propagation from Deceptive News Sources: Who Shares, How Much, How Evenly, and How Quickly?
December 09, 2018 Β· Declared Dead Β· π IEEE Transactions on Computational Social Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Maria Glenski, Tim Weninger, Svitlana Volkova
arXiv ID
1812.03533
Category
cs.SI: Social & Info Networks
Citations
41
Venue
IEEE Transactions on Computational Social Systems
Last Checked
6 months ago
Abstract
As people rely on social media as their primary sources of news, the spread of misinformation has become a significant concern. In this large-scale study of news in social media we analyze eleven million posts and investigate propagation behavior of users that directly interact with news accounts identified as spreading trusted versus malicious content. Unlike previous work, which looks at specific rumors, topics, or events, we consider all content propagated by various news sources. Moreover, we analyze and contrast population versus sub-population behaviour (by demographics) when spreading misinformation, and distinguish between two types of propagation, i.e., direct retweets and mentions. Our evaluation examines how evenly, how many, how quickly, and which users propagate content from various types of news sources on Twitter. Our analysis has identified several key differences in propagation behavior from trusted versus suspicious news sources. These include high inequity in the diffusion rate based on the source of disinformation, with a small group of highly active users responsible for the majority of disinformation spread overall and within each demographic. Analysis by demographics showed that users with lower annual income and education share more from disinformation sources compared to their counterparts. News content is shared significantly more quickly from trusted, conspiracy, and disinformation sources compared to clickbait and propaganda. Older users propagate news from trusted sources more quickly than younger users, but they share from suspicious sources after longer delays. Finally, users who interact with clickbait and conspiracy sources are likely to share from propaganda accounts, but not the other way around.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Social & Info Networks
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Fake News Detection on Social Media: A Data Mining Perspective
R.I.P.
π»
Ghosted
Natural Scales in Geographical Patterns
R.I.P.
π»
Ghosted
Representation Learning on Graphs: Methods and Applications
R.I.P.
π»
Ghosted
The COVID-19 Social Media Infodemic
R.I.P.
π»
Ghosted
OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
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