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Using affordances to improve AI support of social media posting decisions

Authors:

Harmanpreet Kaur,

Walter S. LaseckiAuthors Info & Claims

IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces

Pages 556 - 567

https://doi.org/10.1145/3377325.3377504

Published: 17 March 2020 Publication History

Abstract

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.

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Cited By

Milton AMaestre JRoy AUmbach RChancellor S(2024)Seeking in Cycles: How Users Leverage Personal Information Ecosystems to Find Mental Health InformationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641894(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641894
Ernala SYang SWu YChen RWells KDas S(2021)Exploring the Utility Versus Intrusiveness of Dynamic Audience Selection on FacebookProceedings of the ACM on Human-Computer Interaction10.1145/34760835:CSCW2(1-30)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3476083

Index Terms

Using affordances to improve AI support of social media posting decisions
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
      1. Social networking sites
    2. Collaborative and social computing theory, concepts and paradigms
      1. Social content sharing
      2. Social media
  2. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models

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cover image ACM Conferences

IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces

March 2020

607 pages

ISBN:9781450371186

DOI:10.1145/3377325

General Chairs:
Fabio Paternò,
Nuria Oliver,
Program Chairs:
Cristina Conati,
Lucio Davide Spano,
Nava Tintarev

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 March 2020

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March 17 - 20, 2020

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Cited By

Milton AMaestre JRoy AUmbach RChancellor S(2024)Seeking in Cycles: How Users Leverage Personal Information Ecosystems to Find Mental Health InformationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641894(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641894
Ernala SYang SWu YChen RWells KDas S(2021)Exploring the Utility Versus Intrusiveness of Dynamic Audience Selection on FacebookProceedings of the ACM on Human-Computer Interaction10.1145/34760835:CSCW2(1-30)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3476083

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