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Mind over Metrics: A Neuroscience-Based Framework for Social Media Product Design

datacite.rightsrestricted
dc.contributor.advisorGhazanfar, Asif A.
dc.contributor.authorSirenko, Marie
dc.date.accessioned2025-08-07T14:36:01Z
dc.date.available2025-08-07T14:36:01Z
dc.date.issued2025-05-25
dc.description.abstractSocial media (SM) is an integral part of our lives, shaping the ways we interact with one another and consume information. Both scientific discourse and media narratives have tended to focus broadly on the negative impacts of SM on our wellbeing and cognitive ability. However, recent literature suggests that much of the previous evidence backing these claims has been marked by weak effect sizes and many confounding variables. In order to gain a more nuanced understanding of how SM affects us and rethink the future of these platforms, I bridge the fields of neuroscience and product design to break down the impacts of specific SM features and the cognitive and neural mechanisms behind them. I first analyze the “infinite scroll” feature through the lens of attention to demonstrate how it drives addictive behavior and dissociation, and I suggest ways to reintroduce user agency. I then use frameworks of memory and cognition to assess how SM induces cognitive overload, and I show how recommendation algorithms can reduce this by optimizing content presentation. Finally, I show how the degree of synchronicity and modality of SM communication features shape our perception and the way we connect with others, and I explore how to best replicate face-to-face interaction. Rather than focusing on general outcomes, I shift the conversation by providing a neuroscience-based framework to minimize the negative impacts and maximize the benefits of SM use at the feature level, helping pave the way for more meaningful SM use.
dc.identifier.urihttps://theses-dissertations.princeton.edu/handle/88435/dsp01z029p8184
dc.language.isoen_US
dc.titleMind over Metrics: A Neuroscience-Based Framework for Social Media Product Design
dc.typePrinceton University Senior Theses
dspace.entity.typePublication
dspace.workflow.startDateTime2025-04-25T18:56:23.768Z
pu.contributor.authorid920245107
pu.date.classyear2025
pu.departmentNeuroscience

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