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Thursday, February 18
Thu, Feb 18, 4:00 PM - 5:30 PM
Anomaly Detection

Scaling New Peaks: A Viewership-Centric Approach to Content Curation (304152)

*Subhabrata Majumdar, AT&T Labs Research 
Deirdre Paul, AT&T Labs Research 
Eric Zavesky, AT&T Labs Research 

Keywords: content curation; highlights generation; video streaming; anomaly detection; big data

Summarizing video content is important for video streaming services to engage the user in a limited time span. To this end, current methods involve manual curation or using passive interest cues to annotate potential high-interest segments to form the basis of summarized videos, and are costly and unreliable. We propose a viewership-driven, automated method that accommodates a range of segment identification goals. Using DIRECTV viewership data as a source of ground truth for viewer interest, we apply statistical anomaly detection on a timeline of viewership metrics to identify ‘seed’ segments of high viewer interest. These segments are post-processed using empirical rules and several sources of content metadata, e.g. shot boundaries, adding in personalization aspects to produce the final highlights video.

To demonstrate the flexibility of our approach, we curated highlights of >50 diverse TV shows, including Game of Thrones, Democratic Presidential Debates, and Wimbledon Women’s Final 2019. We perform comparisons with their publicly available highlights, as well as early vs. late viewership comparisons for insights into possible media and social influence on viewing behavior.