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Computer Science > Computer Vision and Pattern Recognition

Title: Do Facial Expressions Predict Ad Sharing? A Large-Scale Observational Study

Abstract: People often share news and information with their social connections, but why do some advertisements get shared more than others? A large-scale test examines whether facial responses predict sharing. Facial expressions play a key role in emotional expression. Using scalable automated facial coding algorithms, we quantify the facial expressions of thousands of individuals in response to hundreds of advertisements. Results suggest that not all emotions expressed during viewing increase sharing, and that the relationship between emotion and transmission is more complex than mere valence alone. Facial actions linked to positive emotions (i.e., smiles) were associated with increased sharing. But while some actions associated with negative emotion (e.g., lip depressor, associated with sadness) were linked to decreased sharing, others (i.e., nose wrinkles, associated with disgust) were linked to increased sharing. The ability to quickly collect facial responses at scale in peoples' natural environment has important implications for marketers and opens up a range of avenues for further research.
Comments: 33 pages
Subjects: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
Cite as: arXiv:1912.10311 [cs.CV]
  (or arXiv:1912.10311v1 [cs.CV] for this version)

Submission history

From: Daniel McDuff [view email]
[v1] Sat, 21 Dec 2019 18:25:57 GMT (552kb)

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