Using affordances to improve AI support of social media posting decisions

Intelligent systems are limited in their ability to match the fluid social needs of people. We use affordances - -people's perceptions of the utilities of a target system - -as a means of creating models that provide intelligent systems with a better understanding of how people make decisions. We study affordance-based models in the context of social network site (SNS) usage, a domain where people have complex social needs often poorly supported by technology. Using data collected via a scenario-based survey (N=674), we build two affordance-based models about people's multi-SNS posting behavior. Our results highlight the feasibility of using affordances to help intelligent systems support people's decision-making behavior: both of our models are ∼15% more accurate than a majority-class baseline, and they are ∼33% and ∼48% more accurate than a random baseline for this task. We contrast our approach with other ways of modeling posting behavior and discuss the implications of using affordances for modeling human behavior for intelligent systems.