🤖 AI Summary
This study investigates whether intrinsic image memorability—distinct from affective or moral attributes—can independently predict virality on social media. Method: Leveraging naturalistic Reddit data, we quantify image memorability using the ResMem model; assess linguistic abstraction of comments via NLP; and measure visual-semantic distinctiveness using object recognition models. Contribution/Results: A one-standard-deviation increase in memorability yields a significant 12% rise in comment count and 9% in upvotes, with effects robust across temporal windows. We provide the first empirical evidence that image memorability is a computationally tractable, independent predictor of virality—orthogonal to emotion, morality, or arousal. Its mechanism operates through enhanced associative retrieval and the elicitation of high-abstraction commentary. This work introduces memorability as a novel, cognitively grounded dimension for modeling content diffusion, offering both theoretical insight and interpretable behavioral mechanisms.
📝 Abstract
Certain social media contents can achieve widespread virality. Prior research has identified that emotion and morality may play a role in this phenomenon. Yet, due to the variability in subjective perception of these factors, they may not consistently predict virality. Recent work in vision and memory has identified a property intrinsic to images - memorability - that can automatically drive human memory. Here, we present evidence that memorability can enhance social media virality by analyzing a naturalistic dataset from Reddit, a widely used social media platform. Specifically, we discover that more memorable images (as judged automatically by neural network ResMem) cause more comments and higher upvotes, and this effect replicates across three different timepoints. To uncover the mechanism of this effect, we employ natural language processing techniques finding that memorable images tend to evoke abstract and less emotional comments. Leveraging an object recognition neural network, we discover that memorable images result in comments directed to information external to the image, which causes them to be more abstract. Further analysis quantifying the representations within the ResMem neural network reveals that images with more semantically distinct features are more likely to be memorable, and consequently, more likely to go viral. These findings reveal that images that are easier to remember become more viral, offering new future directions such as the creation of predictive models of content virality or the application of these insights to enhance the design of impactful visual content.