It is found that early seeding of agents with lower self-censorship not only constrains the spread of manipulation but can also lead to dominance of well-informed populance, which has implications for policies that aim to facilitate healthier discourse, strengthen social cohesion, and ensure equitable access to reliable information.
Abstract
Advanced digital communication has revolutionized how people create and consume information, making information diffusion an important topic of research for domains from public health to national security. Real-world scenarios of information diffusion often involve competing narratives - true and false - spreading simultaneously. We propose a novel agent-based co-diffusion model, grounded in"complex-contagion"and"spiral of silence"theories, to capture how network dynamics exploit cognitive biases to shape such interactions. Our findings reveal that manipulative narratives dominate when early spreaders hold them. These network dynamics further exploit inherent cognitive biases to amplify information diffusion regardless of veracity. Further, while favourable previous experience strengthen collective optimism, unfavourable experiences attenuate optimism only modestly. However, we found that early seeding of agents with lower self-censorship not only constrains the spread of manipulation but can also lead to dominance of well-informed populance. This has implications for policies that aim to facilitate healthier discourse, strengthen social cohesion, and ensure equitable access to reliable information.
Models of social contagion usually assume how individuals adopt beliefs and derive population behavior from it. We instead empirically measure belief adoption in language model agents, quantifying the probability an agent adopts a claim given how many peers endorse it. We find this adoption kernel to be sigmoid, a char...
With the rapid spread of news on social media, understanding the propagation of misinformation is becoming increasingly important. One factor that affects individuals'vulnerability to false information is their ideological predisposition. Despite the large number of agent-based models that focus on social influence as...
S. Karimi, Marcos Oliveira, D. Pacheco· 0 citations
Why do older adults engage more with misinformation online, even when they often identify falsehoods correctly in surveys? This book investigates that paradox using a host of survey experiments and behavioral trace data. Analyses across multiple nationally representative samples show that older Americans disproportio...
We develop a comprehensive framework to evaluate policy interventions aimed at curbing false news dissemination on social media. Using a randomized experiment on Twitter and
X
during the 2022 and 2024 U.S. elections, we assess priming for misinformation awareness, fact‐checking, confirmation clicks, and content...
Sergei M. Guriev, Emeric Henry, Théo Marquis et al.· Econometrica· 0 citations
Generative agent-based models (GABMs) are increasingly used to simulate social media dynamics, including misinformation spread. For such social simulations to be valid proxies of human behavior, LLM agents should replicate established human cognitive biases, among them the Illusory Truth Effect (ITE), where repeated ex...
Agent-based social media simulators offer a controlled environment to study content moderation, yet their value hinges on how faithfully they reproduce real platform dynamics. We develop a calibrated extension of SimSoM, an agent-based model of information diffusion on social networks, grounded in a real-world dataset...
Enrico Verdolotti, Gianluca Nogara, Luca Luceri et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.