All Work Published on Sciences (Social, Health, Biological, Physical)
PsychAdapter lets researchers dial in on personality traits, age, and mental health characteristics to generate text that sounds like real individuals, opening the door to training simulations and personalized content.
PsychAdapter lets researchers dial in on personality traits, age, and mental health characteristics to generate text that sounds like real individuals, opening the door to training simulations and personalized content.
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.
Stanford HAI and the Wu Tsai Neurosciences Institute jointly seek proposals that transform our understanding of the human brain using AI and advance the development of intelligent technology.
Stanford HAI and the Wu Tsai Neurosciences Institute jointly seek proposals that transform our understanding of the human brain using AI and advance the development of intelligent technology.
Stanford scholars respond to a federal RFI related to research ethics, sharing lessons from their experience operating an ethical reflection process for research grants.
Stanford scholars respond to a federal RFI related to research ethics, sharing lessons from their experience operating an ethical reflection process for research grants.
From designing new antibodies to simulating 1,000 years of climate in a day, AI is transforming what's possible—but humans remain the ones deciding what matters.
From designing new antibodies to simulating 1,000 years of climate in a day, AI is transforming what's possible—but humans remain the ones deciding what matters.
