Communications, Media
Generative AI is reshaping communications and media and challenging public trust.
Shana Lynch, HAI Head of Content and Associate Director of Communications, pointed out the "'era of AI evangelism is giving way to an era of AI evaluation,'" in her AI predictions piece, where she interviewed several Stanford AI experts on their insights for AI impacts in 2026.
Shana Lynch, HAI Head of Content and Associate Director of Communications, pointed out the "'era of AI evangelism is giving way to an era of AI evaluation,'" in her AI predictions piece, where she interviewed several Stanford AI experts on their insights for AI impacts in 2026.
We invited 11 sci-fi filmmakers and AI researchers to Stanford for Stories for the Future, a day-and-a-half experiment in fostering new narratives about AI. Researchers shared perspectives on AI and filmmakers reflected on the challenges of writing AI narratives. Together researcher-writer pairs transformed a research paper into a written scene. The challenge? Each scene had to include an AI manifestation, but could not be about the personhood of AI or AI as a threat. Read the results of this project.
We invited 11 sci-fi filmmakers and AI researchers to Stanford for Stories for the Future, a day-and-a-half experiment in fostering new narratives about AI. Researchers shared perspectives on AI and filmmakers reflected on the challenges of writing AI narratives. Together researcher-writer pairs transformed a research paper into a written scene. The challenge? Each scene had to include an AI manifestation, but could not be about the personhood of AI or AI as a threat. Read the results of this project.
Preparing for the Age of Deepfakes and Disinformation
This brief warns of the dangers of generative adversarial networks that can make realistic deepfakes, calling for comprehensive norms, regulations, and laws to counter AI-driven disinformation.
This brief warns of the dangers of generative adversarial networks that can make realistic deepfakes, calling for comprehensive norms, regulations, and laws to counter AI-driven disinformation.
Gathering and analyzing data require time and expertise — two resources that cash-strapped newspapers often don’t have. Can AI help?
Gathering and analyzing data require time and expertise — two resources that cash-strapped newspapers often don’t have. Can AI help?
Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation’s initial spread. Our paper provides a more precise estimate of misinformation’s impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.
Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation’s initial spread. Our paper provides a more precise estimate of misinformation’s impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.
All Work Published on Communications, Media
Social media platforms are too often understood as monoliths with clear priorities. Instead, we analyze them as complex organizations torn between starkly different justifications of their missions. Focusing on the case of Meta, we inductively analyze the company’s public materials and identify three evaluative logics that shape the platform’s decisions: an engagement logic, a public debate logic, and a wellbeing logic. There are clear trade-offs between these logics, which often result in internal conflicts between teams and departments in charge of these different priorities. We examine recent examples showing how Meta rotates between logics in its decision-making, though the goal of engagement dominates in internal negotiations. We outline how this framework can be applied to other social media platforms such as TikTok, Reddit, and X. We discuss the ramifications of our findings for the study of online harms, exclusion, and extraction.
Social media platforms are too often understood as monoliths with clear priorities. Instead, we analyze them as complex organizations torn between starkly different justifications of their missions. Focusing on the case of Meta, we inductively analyze the company’s public materials and identify three evaluative logics that shape the platform’s decisions: an engagement logic, a public debate logic, and a wellbeing logic. There are clear trade-offs between these logics, which often result in internal conflicts between teams and departments in charge of these different priorities. We examine recent examples showing how Meta rotates between logics in its decision-making, though the goal of engagement dominates in internal negotiations. We outline how this framework can be applied to other social media platforms such as TikTok, Reddit, and X. We discuss the ramifications of our findings for the study of online harms, exclusion, and extraction.
This brief introduces a quantitative framework to gauge the effect tax policy or regulatory action would have on Facebook’s platform usage, consumer welfare, and revenues.
This brief introduces a quantitative framework to gauge the effect tax policy or regulatory action would have on Facebook’s platform usage, consumer welfare, and revenues.
A new study by researchers from Stanford, Carnegie Mellon, and Meta finds that the presence of ads on Facebook doesn’t significantly affect how users value the platform.
A new study by researchers from Stanford, Carnegie Mellon, and Meta finds that the presence of ads on Facebook doesn’t significantly affect how users value the platform.
This article cites the Stanford HAI AI Index's data relating to copyright infringement in creative works having to do with AI models.
This article cites the Stanford HAI AI Index's data relating to copyright infringement in creative works having to do with AI models.
Building a Social Media Algorithm That Actually Promotes Societal Values
A Stanford research team shows that building democratic values into a feed-ranking algorithm reduces partisan animosity.
A Stanford research team shows that building democratic values into a feed-ranking algorithm reduces partisan animosity.
