The Age of AI: Audience Segmentation and Predictive Audience Engagement

Advances in artificial intelligence (AI) are influencing both the news industry and individual news consumption behaviors. The ability to convert structured data into a captivating story, indistinguishable from human-authored content, has large implications on the genesis and dynamics of audience segmentation. This project argues that audience fragmentation—accelerated by artificial intelligence—will be qualitatively different from that driven by either the multiplication of channels or on-demand personalized news consumption.

The purpose of the project is threefold: (1) segment today’s news audiences based on their current awareness, understanding and attitudes toward revolutionary changes that are being made in news production and distribution driven by artificial intelligence; (2) examine the audiences’ engagement with news and content powered by AI and automated journalism based on their current uses and gratifications; (3) identify the potentials and limits of AI-powered news and content to provide recommendations for ethically and efficiently incorporating AI technologies and requirements into the news ecosystem in a manner that best serves journalism and its audiences.

With these goals in mind, the current research examines how news audiences are segmented based on the beliefs held, the behaviors enacted, and the constraints faced concerning changes that are being made in news production and distribution powered by artificial intelligence and/or automated journalism.

This project will conduct two rounds of an online survey with adults in the United States. To segment news audiences, the survey data will be analyzed using latent cluster analysis (LCA), a statistical method for identifying unobserved subgroups within populations based on observed indicators. Unlike typical audience segmentation that is ad hoc and crude, the social scientific approach seeks to identify predictable groups based on the empirical observations that appear to be similar across a number of variables and subsequently develop an understanding of the underlying structure in terms of characteristics.

Project lead: Joon Soo Lim

January 01, 2018