research-article

TagRefinery: A Visual Tool for Tag Wrangling

Authors:

Christoph Kralj,

Mohsen Kamalzadeh,

Torsten MöllerAuthors Info & Claims

CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

Pages 2928 - 2939

https://doi.org/10.1145/3025453.3025868

Published: 02 May 2017 Publication History

Abstract

We present TagRefinery, an interactive visual application aiding the cleaning and processing of open tag spaces, such as those in Last.fm or YouTube. Our pre-design analysis showed a need to support a spectrum of user expertise from novice to advanced, which resulted in two distinct interface modes. Summative evaluations of TagRefinery showed that it could effectively guide the novice users through the workflow by giving them brief but helpful explanations on why each step was required, and providing visual and statistical aids to help them in making important decisions. This is while our more expert users greatly appreciated the amount of control and granularity over the workflow that our more advanced interface mode offered. Both the underlying tag cleaning workflow and the interface were designed iteratively in a participatory design process in collaboration with research on a music recommendation interface based on Last.fm tags.

Supplementary Material

ZIP File (pn3128-file4.zip)

Download
13.62 MB

suppl.mov (pn3128-file3.mp4)

Supplemental video

Download
14.76 MB

MP4 File (p2928-kralj.mp4)

Download
141.85 MB

References

[1]

Aaron Bangor, Philip Kortum, and James Miller. 2009. Determining What Individual SUS Scores Mean: Adding an Adjective Rating Scale. J. Usability Studies 4, 3 (May 2009), 114--123. http://dl.acm.org/citation.cfm?id=2835587.2835589

Digital Library

[2]

Dominikus Baur, Jennifer Bttgen, and Andreas Butz. 2012. Listening Factors: A Large-Scale Principal Components Analysis of Long-Term Music Listening Histories. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'09). 1273--1276. http://dl.acm.org/citation.cfm?id=2208581

Digital Library

[3]

Thierry Bertin-Mahieux, Daniel P. W. Ellis, Brian Whitman, and Paul Lamere. 2011. The Million Song Dataset. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR'11). International Society for Music Information Retrieval, Miami, Florida, USA, 591--596. http://ismir2011.ismir.net/papers/OS6--1.pdf

[4]

Eric Brill and Robert C. Moore. 2000. An Improved Error Model for Noisy Channel Spelling Correction. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL'00). Stroudsburg, PA, USA, 286--293.

Digital Library

[5]

John Brooke. 1996. SUS-A quick and dirty usability scale. Usability evaluation in industry 189, 194 (1996), 4--7. http://hell.meiert.org/core/pdf/sus.pdf

[6]

Stefan Evert and Brigitte Krenn. 2005. Using small random samples for the manual evaluation of statistical association measures. Computer Speech and Language 19, 4 (2005), 450--466.

Digital Library

[7]

Afsaneh Fazly, Paul Cook, and Suzanne Stevenson. 2009. Unsupervised Type and Token Identification of Idiomatic Expressions. Computational Linguistics 35, May 2008 (2009), 61--103.

Digital Library

[8]

Davide Fossati and Barbara Di Eugenio. 2008. I saw TREE trees in the park: How to Correct Real-Word Spelling Mistakes. In Proceedings of the International Conference on Language Resources and Evaluation (LREC'08) (28--30). Marrakech, Morocco, 896--901. http://www.lrec-conf.org/proceedings/lrec2008/

[9]

Spence Green, Marie-Catherine de Marneffe, and Christopher D. Manning. 2012. Parsing Models for Identifying Multiword Expressions. Computational Linguistics 39, 1 (Nov 2012), 195--227.

Digital Library

[10]

Manish Gupta, Rui Li, Zhijun Yin, and Jiawei Han. 2010. Survey on social tagging techniques. ACM SigKDD Explorations Newsletter 12, 1 (2010), 58--72.

Digital Library

[11]

Graeme Hirst and Alexander Budanitsky. 2005. Correcting real-word spelling errors by restoring lexical cohesion. Natural Language Engineering 11, December 2003 (2005), 87--111.

Digital Library

[12]

Mohsen Kamalzadeh, Christoph Kralj, Torsten Möller, and Michael Sedlmair. 2016. TagFlip: Active Mobile Music Discovery with Social Tags. In Proceedings of the ACM International Conference on Intelligent User Interfaces (IUI'16). ACM Press, 19--30.

Digital Library

[13]

Yvonne Kammerer, Rowan Nairn, Peter Pirolli, and Ed H. Chi. 2009. Signpost from the Masses: Learning Effects in an Exploratory Social Tag Search Browser. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'09). NY, NY, USA, 625--634.

Digital Library

[14]

Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffrey Heer. 2011. Wrangler: Interactive Visual Specification of Data Transformation Scripts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'011). ACM, NY, NY, USA, 3363--3372.

Digital Library

[15]

Paul Lamere. 2008. Social Tagging and Music Information Retrieval. Journal of New Music Research 37, 2 (2008), 101--114. http://www.tandfonline.com/doi/ abs/10.1080/09298210802479284

[16]

C. Laurier, M. Sordo, Joan Serrà, and Perfecto Herrera. 2009. Music Mood Representations from Social Tags. In Proceedings of the International Society of Music Information Retrieval Conference (ISMIR'09). Kobe, Japan, 381--386. http://mtg.upf.edu/node/1466

[17]

Mark Levy and Mark B. Sandler. 2007. A Semantic Space for Music Derived from Social Tags. In Proceedings of the International Society of Music Information Retrieval Conference (ISMIR'07). Vienna, Austria, 411--416. http://ismir2007.ismir.net/ proceedings/ISMIR2007_p411_levy.pdf

[18]

Emanuele Quintarelli. 2005. Folksonomies: Power to the People. (2005). http: //www-dimat.unipv.it/biblio/isko/doc/folksonomies.htm

[19]

Carlos Ramisch, Helena De Medeiros Caseli, Aline Villavicencio, Andre Machado, and Maria José Finatto. 2010. A hybrid approach for multiword expression identification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6001 LNAI (2010), 65--74.

[20]

Christoph Ringlstetter, Klaus U. Schulz, and Stoyan Mihov. 2006. Orthographic Errors in Web Pages: Toward Cleaner Web Corpora. Computational Linguistics 3, December 2005 (2006), 295--340.

Digital Library

[21]

Ivan A. Sag, Timothy Baldwin, Francis Bond, Ann Copestake, and Dan Flickinger. 2002. Multiword Expressions: A Pain in the Neck for NLP. 1--15.

[22]

Nathan Schneider, Emily Danchik, Chris Dyer, and Noah A. Smith. 2014. Discriminative lexical semantic segmentation with gaps: running the MWE gamut. Transactions of the Association for Computational Linguistics 2 (April 2014), 193--206. http: //www.transacl.org/wp-content/uploads/2014/04/51.pdf

[23]

Ben Shneiderman. 1996. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Proceedings of the IEEE Symposium on Visual Languages (VL'96). 336--343. http://dl.acm.org/citation.cfm?id=832277.834354

Digital Library

[24]

Louise F. Spiteri. 2007. The structure and form of folksonomy tags: The road to the public library catalog. Information Technology and Libraries 26, 3 (2007), 13. http://ejournals.bc.edu/ojs/index.php/ital/article/ view/3272

[25]

Kristina Toutanova and Robert C. Moore. 2002. Pronunciation Modeling for Improved Spelling Correction. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL'02). Association for Computational Linguistics, Stroudsburg, PA, USA, 144--151.

Digital Library

[26]

Tim Van de Cruys and Begoña Villada Moirón. 2007. Semantics-based Multiword Expression Extraction. In Proceedings of the Workshop on a Broader Perspective on Multiword Expressions (MWE'07). 25--32. http://dl.acm.org/citation.cfm?id=1613704.1613708

[27]

Jesse Vig, Shilad Sen, and John Riedl. 2011. Navigating the tag genome. In Proceedings of the ACM International Conference on Intelligent User Interfaces (IUI'11). 93--102. http://doi.acm.org/10.1145/1502650.1502661

Digital Library

[28]

Ju-Chiang Wang, Yu-Chin Shih, Meng-Sung Wu, Hsin-Min Wang, and Shyh-Kang Jeng. 2011. Colorizing Tags in Tag Cloud: A Novel Query-by-Tag Music Search System. In Proceedings of the ACM Conference on Multimedia (MM'11).

Digital Library

Cited By

Salminen JJung SJansen B(2021)Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME52200.2021.9591046(1-7)Online publication date: 7-Oct-2021
https://doi.org/10.1109/ICECCME52200.2021.9591046

Index Terms

TagRefinery: A Visual Tool for Tag Wrangling

Recommendations

Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

The work involved in gathering, wrangling, cleaning, and otherwise preparing data for analysis is often the most time consuming and tedious aspect of data work. Although many studies describe data preparation within the context of data science workflows,...
Automatic tag expansion using visual similarity for photo sharing websites

In this paper we present an automatic photo tag expansion method designed for photo sharing websites. The purpose of the method is to suggest tags that are relevant to the visual content of a given photo at upload time. Both textual and visual cues are ...
Finding similar items by leveraging social tag clouds
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

Recently social collaboration projects such as Wikipedia and Flickr have been gaining popularity, and more and more social tag information is being accumulated. In this study, we demonstrate how to effectively use social tags created by humans to find ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

May 2017

7138 pages

ISBN:9781450346559

DOI:10.1145/3025453

General Chairs:
Gloria Mark
University of California Irvine
,
Susan Fussell
Cornell University
,
Program Chairs:
Cliff Lampe
University of Michigan
,
m.c. schraefel
University of Southampton
,
Juan Pablo Hourcade
University of Iowa
,
Caroline Appert
Université Paris-Sud
,
Daniel Wigdor
University of Toronto

Copyright © 2017 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 the author(s) 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].

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 May 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CHI '17

Sponsor:

SIGCHI

CHI '17: CHI Conference on Human Factors in Computing Systems

May 6 - 11, 2017

Colorado, Denver, USA

Acceptance Rates

CHI '17 Paper Acceptance Rate 600 of 2,400 submissions, 25%;

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

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
395
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)1

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Salminen JJung SJansen B(2021)Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME52200.2021.9591046(1-7)Online publication date: 7-Oct-2021
https://doi.org/10.1109/ICECCME52200.2021.9591046

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten