research-article

Personalization via friendsourcing

Authors:

Michael S. Bernstein,

Mary Czerwinski,

Eric HorvitzAuthors Info & Claims

ACM Transactions on Computer-Human Interaction (TOCHI), Volume 17, Issue 2

Article No.: 6, Pages 1 - 28

https://doi.org/10.1145/1746259.1746260

Published: 21 May 2008 Publication History

Abstract

When information is known only to friends in a social network, traditional crowdsourcing mechanisms struggle to motivate a large enough user population and to ensure accuracy of the collected information. We thus introduce friendsourcing, a form of crowdsourcing aimed at collecting accurate information available only to a small, socially-connected group of individuals. Our approach to friendsourcing is to design socially enjoyable interactions that produce the desired information as a side effect.

We focus our analysis around Collabio, a novel social tagging game that we developed to encourage friends to tag one another within an online social network. Collabio encourages friends, family, and colleagues to generate useful information about each other. We describe the design space of incentives in social tagging games and evaluate our choices by a combination of usage log analysis and survey data. Data acquired via Collabio is typically accurate and augments tags that could have been found on Facebook or the Web. To complete the arc from data collection to application, we produce a trio of prototype applications to demonstrate how Collabio tags could be utilized: an aggregate tag cloud visualization, a personalized RSS feed, and a question and answer system. The social data powering these applications enables them to address needs previously difficult to support, such as question answering for topics comprehensible only to a few of a user's friends.

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Index Terms

Personalization via friendsourcing
1. Human-centered computing
  1. Collaborative and social computing

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Transactions on Computer-Human Interaction

ACM Transactions on Computer-Human Interaction Volume 17, Issue 2

May 2010

129 pages

ISSN:1073-0516

EISSN:1557-7325

DOI:10.1145/1746259

Issue’s Table of Contents

Copyright © 2008 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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Publication History

Accepted: 01 February 2010

Revised: 01 January 2010

Received: 01 July 2009

Published: 21 May 2008

Published in TOCHI Volume 17, Issue 2

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https://dl.acm.org/doi/10.1145/3637314
Nguyen HGhanem T(2023)A Classification Approach to Detect Social Support in Online Health Communities2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386442(4891-4899)Online publication date: 15-Dec-2023
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González DTansini L(2022)Analyzing the Efficiency of Recommender Systems Using Machine LearningInformation Systems and Technologies10.1007/978-3-031-04826-5_68(692-698)Online publication date: 11-May-2022
https://doi.org/10.1007/978-3-031-04826-5_68
Rubya SNumainville JYarosh SKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Comparing Generic and Community-Situated Crowdsourcing for Data Validation in the Context of Recovery from Substance Use DisordersProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445399(1-17)Online publication date: 6-May-2021
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