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Modeling Dwell Time Engagement on Visual Multimedia

Hemank Lamba, Neil Shah
EventKDD 2019
Research AreasData Science

Abstract: Visual multimedia is one of the most prevalent sources of modern online content and engagement. However, despiteits prevalence, littleis known about user engagement with such content. For instance, how can we model engagement for a specific content or viewer sample, and across multiple samples? Can we model and discover patterns in these interactions, and detect outlying behaviors corresponding to abnormal engagement? In this paper, we study these questions in depth. Understanding these questions has implications in user modeling and understanding, ranking, trust and safety and more. For analysis, we consider content and viewer dwell time (engagement duration) behaviors with images and videos on Snapchat Stories, one of the largest multimedia-driven social sharing services. To our knowledge, we are the first to model and analyze dwell time behaviors on such media. Specifically, our contributions include (a) individual modeling: we propose and evaluate theUm-Dp, Lm-Dp andV-Dp parametric models to describe dwell times of unlooped/looped media and viewers which outperform alternatives, (b) aggregate modeling: we show how to flexibly summarize the respective joint distributions of multivariate parametrized fits across many samples using Vine Copulas in the analog Um-Am, Lm-Am and V-Am models, which enable inferences regarding aggregate behavioral patterns, and offer the ability to simulate real-looking engagement data (c) anomaly detection: we demonstrate our aggregate models can robustly detect anomalies present during training (0.9+ AUROC across most attack models), and also enable discovery of real dwell time anomalies.

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