Fetching the paper…
Reading the bibliography…
In a pre-registered algorithmic audit, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm amplifies emotionally charged, out-group hostile content that users say makes them feel worse about their political out-group.
Adaptive linear step-up procedures that control the false discovery rate
Yoav Benjamini, Abba M Krieger, and Daniel Yekutieli · 2006
Earlier work this paper cites.
Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects
Michael L Anderson · 2008
Earlier work this paper cites.
Exposure to ideologically diverse news and opinion on Facebook
Eytan Bakshy, Solomon Messing, and Lada A Adamic · 2015
Earlier work this paper cites.
Deep Neural Networks for Youtube Recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
Earlier work this paper cites.
Behaviorism is not enough: Better recommendations through listening to users
Michael D. Ekstrand and Martijn C. Willemsen · 2016
Earlier work this paper cites.
Emotion shapes the diffusion of moralized content in social networks
William J Brady, Julian A Wills, John T Jost, Joshua A Tucker, and Jay J Van Bavel · 2017
Earlier work this paper cites.
YouTube, The Great Radicalizer
Zeynep Tufekci · 2018
Earlier work this paper cites.
An ideological asymmetry in the diffusion of moralized content on social media among political leaders
William J Brady, Julian A Wills, Dominic Burkart, John T Jost, and Jay J Van Bavel · 2019
Earlier work this paper cites.
The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online
William J. Brady and M. J. Crockett and Jay J. Van Bavel · 2020
Earlier work this paper cites.
More Accounts, Fewer Links: How Algorithmic Curation Impacts Media Exposure in Twitter Timelines
Jack Bandy and Nicholas Diakopoulos · 2021
Earlier work this paper cites.
Estimating the effect size of moral contagion in online networks: A pre-registered replication and meta-analysis [Preprint], Apr 2021
William J Brady and Jay J Van Bavel · 2021
Earlier work this paper cites.
Reconsidering evidence of moral contagion in online social networks
Jason W Burton, Nicole Cruz, and Ulrike Hahn · 2021
Earlier work this paper cites.
Data quality of platforms and panels for online behavioral research
Peer Eyal, Rothschild David, Gordon Andrew, Evernden Zak, and Damer Ekaterina · 2021
Earlier work this paper cites.
Examining the consumption of radical content on youtube
Homa Hosseinmardi, Amir Ghasemian, Aaron Clauset, Markus Mobius, David M. Rothschild, and Duncan J. Watts · 2021
Earlier work this paper cites.
From Optimizing Engagement to Measuring Value
Smitha Milli, Luca Belli, and Moritz Hardt · 2021
Earlier work this paper cites.
Out-group animosity drives engagement on social media
Steve Rathje, Jay J Van Bavel, and Sander Van Der Linden · 2021
Earlier work this paper cites.
How TikTok reads your mind, Dec 2021
Ben Smith · 2021
Cited alongside, same era.
Causal Inference Struggles with Agency on Online Platforms
Smitha Milli, Luca Belli, and Moritz Hardt · 2022
Cited alongside, same era.
Most users do not follow political elites on Twitter; those who do show overwhelming preferences for ideological congruity
Magdalena Wojcieszak, Andreu Casas, Xudong Yu, Jonathan Nagler, and Joshua A. Tucker · 2022
Cited alongside, same era.
Automating Automaticity: How the Context of Human Choice Affects the Extent of Algorithmic Bias
Amanda Y Agan, Diag Davenport, Jens Ludwig, and Sendhil Mullainathan · 2023
Cited alongside, same era.
ANES 2020 Time Series Study Full Release
American National Election Studies · 2023
Cited alongside, same era.
Exposure to Marginally Abusive Content on Twitter
Jack Bandy and Tomo Lazovich · 2023
Can Algorithmic Recommendation Systems Be Good For Democracy? (Yes! & Chronological Feeds May Be Bad)
Aviv Ovadya · 2023
Closest in time.
The Amplification Paradox in Recommender Systems
Manoel Horta Ribeiro, Veniamin Veselovsky, and Robert West · 2023
Closest in time.
Whose Opinions Do Language Models Reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto · 2023
Closest in time.
Yes, Elon Musk created a special system for showing you all his tweets first
Zoë Schiffer and Casey Newton · 2023
Closest in time.
Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles
Ivan Srba, Robert Moro, Matus Tomlein, Branislav Pecher, Jakub Simko, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, Adrian Gavornik, and Maria Bielikova · 2023
Closest in time.
From “Filter Bubbles”, “Echo Chambers”, and “Rabbit Holes” to “Feedback Loops”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Crowdsourced audit of Twitter’s recommender systems
Paul Bouchaud, David Chavalarias, and Maziyar Panahi · 2023
Cited alongside, same era.
User Strategization and Trustworthy Algorithms
Sarah H Cen, Andrew Ilyas, and Aleksander Madry · 2023
Cited alongside, same era.
Subscriptions and external links help drive resentful users to alternative and extremist youtube channels
Annie Y. Chen, Brendan Nyhan, Jason Reifler, Ronald E. Robertson, and Christo Wilson · 2023
Cited alongside, same era.
Data quality in online human-subjects research: Comparisons between mturk, prolific, cloudresearch, qualtrics, and sona
Benjamin D. Douglas, Patrick J. Ewell, and Markus Brauer · 2023
Cited alongside, same era.
Your article is false and obviously so. Do you really do no research at all? I mean, like reading a few tweets, for example
@elonmusk · 2023
Cited alongside, same era.
Asymmetric ideological segregation in exposure to political news on Facebook
Sandra González-Bailón, David Lazer, Pablo Barberá, Meiqing Zhang, Hunt Allcott, Taylor Brown, Adriana Crespo-Tenorio, Deen Freelon, Matthew Gentzkow, Andrew M Guess, et al · 2023
Cited alongside, same era.
Luke Thorburn, Jonathan Stray, and Priyanjana Bengani · 2023
Closest in time.
Twitter’s recommendation algorithm: Heavy ranker and twhin embeddings, Mar 2023
Twitter · 2023
Closest in time.
Partisanship on Social Media: In-Party Love Among American Politicians, Greater Engagement with Out-Party Hate Among Ordinary Users
Xudong Yu, Magdalena Wojcieszak, and Andreu Casas · 2023
Closest in time.
System-2 recommenders: Disentangling utility and engagement in recommendation systems via temporal point-processes
Arpit Agarwal, Nicolas Usunier, Alessandro Lazaric, and Maximilian Nickel · 2024
Closest in time.
What We Know About Using Non-Engagement Signals in Content Ranking, 2024
Tom Cunningham, Sana Pandey, Leif Sigerson, Jonathan Stray, Jeff Allen, Bonnie Barrilleaux, Ravi Iyer, Smitha Milli, Mohit Kothari, and Behnam Rezaei · 2024
Closest in time.
Questioning the Survey Responses of Large Language Models
Ricardo Dominguez-Olmedo, Moritz Hardt, and Celestine Mendler-Dünner · 2024
Closest in time.
Causally estimating the effect of youtube’s recommender system using counterfactual bots
Homa Hosseinmardi, Amir Ghasemian, Miguel Rivera-Lanas, Manoel Horta Ribeiro, Robert West, and Duncan J. Watts · 2024
Closest in time.
Embedding Democratic Values into Social Media AIs via Societal Objective Functions
Chenyan Jia, Michelle S Lam, Minh Chau Mai, Jeffrey T Hancock, and Michael S Bernstein · 2024
Closest in time.
People Think That Social Media Platforms Do (but Should Not) Amplify Divisive Content
Steve Rathje, Claire Robertson, William J. Brady, and Jay J. Van Bavel · 2024
Closest in time.
Non-News Websites Expose People to More Political Content Than News Websites: Evidence from Browsing Data in Three Countries
Magdalena Wojcieszak, Ericka Menchen-Trevino, Bernhard Clemm von Hohenberg, Sjifra de Leeuw, João Gonçalves, Sam Davidson, and Alexandre Gonçalves · 2024
Closest in time.