research-article

Predicting tie strength with social media

Authors:

Karrie KarahaliosAuthors Info & Claims

CHI '09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 211 - 220

https://doi.org/10.1145/1518701.1518736

Published: 04 April 2009 Publication History

Abstract

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.

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Barile FCau FTintarev NBoratto LGena CMarras MGermanakos PPopescus E(2024)A Preliminary Study of the Impact of Personality on Satisfaction in Group ContextsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664893(319-328)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664893
Akallal YHdioud BHaj Thami R(2024)Content and link Analysis of a Moroccan entrepreneurs group2024 International Conference on Intelligent Systems and Computer Vision (ISCV)10.1109/ISCV60512.2024.10620109(1-8)Online publication date: 8-May-2024
https://doi.org/10.1109/ISCV60512.2024.10620109
Jin JHe YLin CDiao L(2024)How tie strength influences purchasing intention in social recommendation: evidence from behavioral model and brain activityInternet Research10.1108/INTR-06-2023-0506Online publication date: 13-Feb-2024
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Index Terms

Predicting tie strength with social media
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction

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cover image ACM Conferences

CHI '09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2009

2426 pages

ISBN:9781605582467

DOI:10.1145/1518701

General Chairs:
Dan R. Olsen
Brigham Young University
,
Richard B. Arthur
Brigham Young University
,
Program Chairs:
Ken Hinckley
Microsoft Research
,
Meredith Ringel Morris
Microsoft Research
,
Scott Hudson
Carnegie Mellon University
,
Saul Greenberg
University of Calgary

Copyright © 2009 ACM.

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Published: 04 April 2009

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April 4 - 9, 2009

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CHI '09 Paper Acceptance Rate 277 of 1,130 submissions, 25%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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https://dl.acm.org/doi/10.1145/3631700.3664893
Akallal YHdioud BHaj Thami R(2024)Content and link Analysis of a Moroccan entrepreneurs group2024 International Conference on Intelligent Systems and Computer Vision (ISCV)10.1109/ISCV60512.2024.10620109(1-8)Online publication date: 8-May-2024
https://doi.org/10.1109/ISCV60512.2024.10620109
Jin JHe YLin CDiao L(2024)How tie strength influences purchasing intention in social recommendation: evidence from behavioral model and brain activityInternet Research10.1108/INTR-06-2023-0506Online publication date: 13-Feb-2024
https://doi.org/10.1108/INTR-06-2023-0506
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Higashi SAmpririt PQafzezi EIkeda MMatsuo KBarolli L(2024)A Fuzzy-Based System for Assessment of Tie Strength in Online Social NetworksAdvances on P2P, Parallel, Grid, Cloud and Internet Computing10.1007/978-3-031-76462-2_12(133-141)Online publication date: 17-Nov-2024
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