In the attention economy, understanding how individuals manage limited attention is critical. We introduce a simple model describing the decay of a user's engagement when facing multiple inputs. We analytically show that individual attention decay is determined by the overall duration of interactions, not their number or user activity. Our model is validated using data from Reddit's Change My View subreddit, where the user's attention dynamics is explicitly traceable. Despite its simplicity, our model offers a crucial microscopic perspective complementing macroscopic studies [1].
Beyond attention economy, we also investigate the role played by the attention of individuals in the reception of art. While certain artists and works achieve enduring success, others fade despite showing technical mastery or cultural relevance [2]. We extracted 63M ratings submitted by online users from 8855 movies and built the timeseries of ratings for each movie. By combining attention and score, we identify well-defined classes of movies depending on their reception pattern. We find very strong correlations in the metadata of movies across the reception classes. Supervised and unsupervised learning algorithms are used to uncover the most important features in the classification task. As a result, we predict the reception of a movie from the audience with high accuracy based solely on their metadata.
[1] J. Ojer et al. arXiv:2507.01511 (2026).
[2] S. P. Fraiberger et al. Science 362, 825 (2018).
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