Journal Article10.1093/joc/jqac051
Visual misinformation on Facebook
43
TL;DR: This article conducted a large-scale study of image-based political misinformation on Facebook, collecting 13,723,654 posts from 14,532 pages and 11,454 public groups from August through October 2020.
read more
Abstract:
We conduct the first large-scale study of image-based political misinformation on Facebook. We collect 13,723,654 posts from 14,532 pages and 11,454 public groups from August through October 2020, posts that together account for nearly all engagement of U.S. public political content on Facebook. We use perceptual hashing to identify duplicate images and computer vision to identify political figures. Twenty-three percent of sampled political images (N = 1,000) contained misinformation, as did 20% of sampled images (N = 1,000) containing political figures. We find enormous partisan asymmetry in misinformation posts, with right-leaning images 5–8 times more likely to be misleading, but little evidence that misleading images generate higher engagement. Previous scholarship, which mostly cataloged links to noncredible domains, has ignored image posts which account for a higher volume of misinformation. This research shows that new computer-assisted methods can scale to millions of images, and help address perennial and long-unanswered calls for more systematic study of visual political communication.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
The impact of generative artificial intelligence on socioeconomic inequalities and policy making
Valerio Capraro,Austin Lentsch,Daron Acemoğlu,Selin Akgün,Aisel Akhmedova,Ennio Bilancini,Jean‐François Bonnefon,Pablo Brañas‐Garza,Luigi Butera,Karen M. Douglas,Jim A. C. Everett,Laura Martignon,Christine Greenhow,Daniel A. Hashimoto,Julianne Holt‐Lunstad,Jolanda Jetten,Simon Johnson,Werner H. Kunz,Chiara Longoni,Pete Lunn,Simone Natale,Stefanie Paluch,Iyad Rahwan,Neil Selwyn,V. Singh,Siddharth Suri,Jennifer Sutcliffe,Joe Tomlinson,Sander van der Linden,Paul A. M. Van Lange,Friederike Wall,Jay Joseph Van Bavel,Riccardo Viale +32 more
TL;DR: Generative AI has the potential to exacerbate and ameliorate socioeconomic inequalities across various domains, including information, work, education, and healthcare. It can democratize content creation and access, boost productivity and create new jobs, personalize learning, improve diagnostics and accessibility, but also widen the digital divide and deepen pre-existing inequalities. Policymaking plays a crucial role in maximizing generative AI's potential to reduce inequalities while mitigating its harmful effects.
45
The impact of generative artificial intelligence on socioeconomic inequalities and policy making
Valerio Capraro,Austin Lentsch,Daron Acemoglu,Selin Akgün,Aisel Akhmedova,Ennio Bilancini,Jean-François Bonnefon,Pablo Brañas-Garza,Luigi Butera,Karen M. Douglas,Jim A.C. Everett,Gerd Gigerenzer,Christine Greenhow,Daniel A. Hashimoto,Julianne Holt-Lunstad,Jolanda Jetten,Simon Johnson,Chiara Longoni,Pete Lunn,Simone Natale,Iyad Rahwan,Neil Selwyn,Vivek Singh,Siddharth Suri,Jennifer Sutcliffe,Joe Tomlinson,S. V. D. Linden,Paul A. M. Van Lange,Friederike Wall,Jay J. Van Bavel,Riccardo Viale +30 more
TL;DR: A state-of-the-art interdisciplinary overview of the probable impacts of generative AI on four critical domains: work, education, health, and information, to warn about how generative AI could worsen existing inequalities while illuminating directions for using AI to resolve pervasive social problems.
Quantifying the impact of misinformation and vaccine-skeptical content on Facebook
TL;DR: The low uptake of the COVID-19 vaccine in the US is partly due to misinformation and vaccine-skeptical content on Facebook.
31
Misinformation in Virtual Reality
TL;DR: In this article , Trauthig and Woolley pointed out that the potential for VR to amplify misinformation is the core threat of VR to trust and safety, and pointed out how VR can be used for manipulation.
The impact of generative artificial intelligence on socioeconomic inequalities and policy making
Valerio Capraro,Austin Lentsch,Daron Acemoğlu,Selin Akgün,Aisel Akhmedova,Ennio Bilancini,Jean‐François Bonnefon,Pablo Brañas‐Garza,Luigi Butera,Karen M. Douglas,Jim A. C. Everett,Laura Martignon,Christine Greenhow,Daniel A. Hashimoto,Julianne Holt‐Lunstad,Jolanda Jetten,Simon Johnson,Chiara Longoni,Pete Lunn,Simone Natale,Iyad Rahwan,Neil Selwyn,Abhishek Singh,Siddharth Suri,Jennifer Sutcliffe,Joe Tomlinson,Sander van der Linden,Paul A. M. Van Lange,Friederike Wall,Jay Joseph Van Bavel,Riccardo Viale +30 more
- 16 Dec 2023
TL;DR: Generative AI has the potential to exacerbate and ameliorate existing socioeconomic inequalities across various domains, including work, education, health, and information. To harness its benefits and mitigate its negative effects, policymaking interventions are required.
References
Power-Law Distributions in Empirical Data
TL;DR: This work proposes a principled statistical framework for discerning and quantifying power-law behavior in empirical data by combining maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios.
Power-law distributions in empirical data
TL;DR: In this article, the authors present a principled statistical framework for discerning and quantifying power-law behavior in empirical data, which combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios.
7.4K
The spread of true and false news online
TL;DR: A large-scale analysis of tweets reveals that false rumors spread further and faster than the truth, and false news was more novel than true news, which suggests that people were more likely to share novel information.
7.2K
Social Media and Fake News in the 2016 Election
Hunt Allcott,Matthew Gentzkow +1 more
TL;DR: The authors found that people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks, and that the average American adult saw on the order of one or perhaps several fake news stories in the months around the 2016 U.S. presidential election, with just over half of those who recalled seeing them believing them.
A Brief History of Generative Models for Power Law and Lognormal Distributions
TL;DR: A rich and long history is found of how lognormal distributions have arisen as a possible alternative to power law distributions across many fields, focusing on underlying generative models that lead to these distributions.