Article

Why we tag: motivations for annotation in mobile and online media

Authors:

Mor NaamanAuthors Info & Claims

CHI '07: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 971 - 980

https://doi.org/10.1145/1240624.1240772

Published: 29 April 2007 Publication History

Abstract

Why do people tag? Users have mostly avoided annotating media such as photos -- both in desktop and mobile environments -- despite the many potential uses for annotations, including recall and retrieval. We investigate the incentives for annotation in Flickr, a popular web-based photo-sharing system, and ZoneTag, a cameraphone photo capture and annotation tool that uploads images to Flickr. In Flickr, annotation (as textual tags) serves both personal and social purposes, increasing incentives for tagging and resulting in a relatively high number of annotations. ZoneTag, in turn, makes it easier to tag cameraphone photos that are uploaded to Flickr by allowing annotation and suggesting relevant tags immediately after capture.

A qualitative study of ZoneTag/Flickr users exposed various tagging patterns and emerging motivations for photo annotation. We offer a taxonomy of motivations for annotation in this system along two dimensions (sociality and function), and explore the various factors that people consider when tagging their photos. Our findings suggest implications for the design of digital photo organization and sharing applications, as well as other applications that incorporate user-based annotation.

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Index Terms

Why we tag: motivations for annotation in mobile and online media
1. Human-centered computing

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cover image ACM Conferences

CHI '07: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2007

1654 pages

ISBN:9781595935939

DOI:10.1145/1240624

General Chair:
Mary Beth Rosson
The Pennsylvania State University, USA
,
Program Chair:
David Gilmore
Intel, USA

Copyright © 2007 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: 29 April 2007

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CHI07: CHI Conference on Human Factors in Computing Systems

April 28 - May 3, 2007

California, San Jose, USA

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CHI '07 Paper Acceptance Rate 182 of 840 submissions, 22%;

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

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https://dl.acm.org/doi/10.1145/3627508.3638295
Naeimi Sadigh ABahraini TSadoghi Yazdi H(2024)Semantic labeling of social big media using distributed online robust classificationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.107928132(107928)Online publication date: Jun-2024
https://doi.org/10.1016/j.engappai.2024.107928
Fazal NFränti P(2024)Relevant Tag Extraction Based on Image Visual ContentApplied Intelligence10.1007/978-981-97-0827-7_25(283-295)Online publication date: 1-Mar-2024
https://doi.org/10.1007/978-981-97-0827-7_25
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