• IEEE.org
  • IEEE CS Standards
  • Career Center
  • About Us
  • Subscribe to Newsletter

0

IEEE-CS_LogoTM-orange
  • MEMBERSHIP
  • CONFERENCES
  • PUBLICATIONS
  • EDUCATION & CAREER
  • VOLUNTEER
  • ABOUT
  • Join Us
IEEE-CS_LogoTM-orange

0

IEEE Computer Society Logo
Sign up for our newsletter
IEEE COMPUTER SOCIETY
About UsBoard of GovernorsNewslettersPress RoomIEEE Support CenterContact Us
COMPUTING RESOURCES
Career CenterCourses & CertificationsWebinarsPodcastsTech NewsMembership
BUSINESS SOLUTIONS
Corporate PartnershipsConference Sponsorships & ExhibitsAdvertisingRecruitingDigital Library Institutional Subscriptions
DIGITAL LIBRARY
MagazinesJournalsConference ProceedingsVideo LibraryLibrarian Resources
COMMUNITY RESOURCES
GovernanceConference OrganizersAuthorsChaptersCommunities
POLICIES
PrivacyAccessibility StatementIEEE Nondiscrimination PolicyIEEE Ethics ReportingXML Sitemap

Copyright 2026 IEEE - All rights reserved. A public charity, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity.

  • Home
  • /Publications
  • /Tech News
  • /Research
  • Home
  • / ...
  • /Tech News
  • /Research

Audience Behavior Mining: When You Watch Television, Data Scientists Are Watching You

By Lori Cameron

By Lori Cameron on
July 5, 2017

pressing button on tv remote control

It might not surprise you to know that people love to watch animal videos. But did you know that people are more likely to watch if the animal is splashing around in water?

This is the kind of information you can mine and analyze from audience behavior data gathered from TV ratings and multimedia content.

Researchers are keenly interested in what captures and loses a viewer’s attention because this information can be used to create better television programming, produce higher ratings, and attract more sponsors, say authors Ryota Hinami of University of Tokyo and Shin’ichi Satoh of National Institute of Informatics.

"To discover relationships between TV ratings and multimedia content, we focus on the change points—that is, the points in time when people first tune in to a particular TV program. Because these points reflect the active intention of TV viewers, they contain valuable information about viewers’ interests. We describe these points using visual features extracted from video and using keywords extracted from transcripts," they write.

Read more about how researchers are fine-tuning the algorithms that reveal what we choose to watch in the April–June 2017 issue of IEEE MultiMedia.

Read article (login may be required for full text)


About Lori Cameron

Lori Cameron is a Senior Writer for the IEEE Computer Society and currently writes regular features for Computer magazine, Computing Edge, and the Computing Now and Magazine Roundup websites. Contact her at l.cameron@computer.org. Follow her on LinkedIn.

LATEST NEWS
AI-Accelerated Quantum Cryptography: How Soon Should the Enterprise Be Ready?
AI-Accelerated Quantum Cryptography: How Soon Should the Enterprise Be Ready?
Computing’s Top 30: Ming Jin
Computing’s Top 30: Ming Jin
IEEE Computer Society Drives AI Innovation at 24-Hour Hackathon
IEEE Computer Society Drives AI Innovation at 24-Hour Hackathon
Computing’s Top 30: Meng Li
Computing’s Top 30: Meng Li
Why Quantum Error Correction Has Become a Full-Stack Engineering Problem
Why Quantum Error Correction Has Become a Full-Stack Engineering Problem
Get the latest news and technology trends for computing professionals with ComputingEdge
Sign up for our newsletter
Read Next

AI-Accelerated Quantum Cryptography: How Soon Should the Enterprise Be Ready?

Computing’s Top 30: Ming Jin

IEEE Computer Society Drives AI Innovation at 24-Hour Hackathon

Computing’s Top 30: Meng Li

Why Quantum Error Correction Has Become a Full-Stack Engineering Problem

Episode 5 | How to Grow Your Career in SAP Supply Chain

IEEE Computer Society Announces New Executive Director

How Can Technology Improve Student Collaboration in Computer Science? An Interview with Bowen Hui