research-article

#nowplaying Music Dataset: Extracting Listening Behavior from Twitter

Authors:

Wolfgang Gassler,

Günther SpechtAuthors Info & Claims

WISMM '14: Proceedings of the First International Workshop on Internet-Scale Multimedia Management

Pages 21 - 26

https://doi.org/10.1145/2661714.2661719

Published: 07 November 2014 Publication History

Abstract

The extraction of information from online social networks has become popular in both industry and academia as these data sources allow for innovative applications. However, in the area of music recommender systems and music information retrieval, respective data is hardly exploited. In this paper, we present the #nowplaying dataset, which leverages social media for the creation of a diverse and constantly updated dataset, which describes the music listening behavior of users. For the creation of the dataset, we rely on Twitter, which is frequently facilitated for posting which music the respective user is currently listening to. From such tweets, we extract track and artist information and further metadata. The dataset currently comprises 49 million listening events, 144,011 artists, 1,346,203 tracks and 4,150,615 users which makes it considerably larger than existing datasets.

References

[1]

F. Abel, C. Hauff, G.-J. Houben, R. Stronkman, and K. Tao. Semantics+Filtering+Search=twitcident. Exploring Information in Social Web Streams. In Proc. of the 23rd ACM Conference on Hypertext and Social Media, pages 285--294. ACM, 2012.

Digital Library

[2]

N. Aizenberg, Y. Koren, and O. Somekh. Build Your Own Music Recommender by Modeling Internet Radio Streams. In Proc. of the 21st Int. Conference on World Wide Web, WWW '12, pages 1--10, New York, NY, USA, 2012. ACM.

Digital Library

[3]

T. Bertin-Mahieux, D. P. Ellis, B. Whitman, and P. Lamere. The Million Song Dataset. In Proc. of the 12th Intl. Conference on Music Information Retrieval (ISMIR 2011), 2011.

[4]

J. G. Breslin, S. Decker, A. Harth, and U. Bojars. SIOC: An Approach to Connect Web-based Communities. Int. Journal of Web Based Communities, 2(2):133--142, 2006.

Digital Library

[5]

I. Cantador, P. Brusilovsky, and T. Kuflik. 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011). In Proc. of the 5th ACM Conference on Recommender systems, RecSys 2011, New York, NY, USA, 2011. ACM.

Digital Library

[6]

O. Celma. Music Recommendation and Discovery in the Long Tail. Springer, 2010.

Digital Library

[7]

S. Dooms, T. De Pessemier, and L. Martens. MovieTweetings: a Movie Rating Dataset Collected From Twitter. In Workshop on Crowdsourcing and Human Computation for Recommender Systems, 2013.

[8]

D. Eastlake and P. Jones. US secure hash algorithm 1 (SHA1), 2001.

[9]

J. Hannon, M. Bennett, and B. Smyth. Recommending Twitter Users to Follow Using Content and Collaborative Filtering Approaches. In Proc. of the fourth ACM Conference on Recommender Systems, pages 199--206. ACM, 2010.

Digital Library

[10]

D. Hauger, M. Schedl, A. Kosir, and M. Tkalcic. The Million Musical Tweet Dataset - What We Can Learn From Microblogs. In Proc. of the 14th Intl. Conference on Music Information Retrieval (ISMIR 2013), pages 189--194, 2013.

[11]

last.fm API. http://www.lastfm.de/api.

[12]

O. Phelan, K. McCarthy, and B. Smyth. Using Twitter to Recommend Real-time Topical News. In Proc. of the Third ACM Conference on Recommender Systems, RecSys '09, pages 385--388, New York, NY, USA, 2009. ACM.

Digital Library

[13]

Y. Raimond, S. A. Abdallah, M. B. Sandler, and F. Giasson. The Music Ontology. In Proc. of the 8th Intl. Conference on Music Information Retrieval (ISMIR 2007), pages 417--422. Citeseer, 2007.

[14]

Y. Raimond and M. B. Sandler. A Web of Musical Information. In Proc. of the 9th Intl. Conference on Music Information Retrieval (ISMIR 2008), pages 263--268, 2008.

[15]

T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors. In Proc. of the 19th Int. Conference on World wide web, pages 851--860. ACM, 2010.

Digital Library

[16]

M. Schedl. Leveraging Microblogs for Spatiotemporal Music Information Retrieval. In Proc. of the 35th European Conference on Advances in Information Retrieval, ECIR'13, pages 796--799, Berlin, Heidelberg, 2013. Springer-Verlag.

Digital Library

[17]

M. Schedl, D. Hauger, and J. Urbano. Harvesting Microblogs for Contextual Music Similarity Estimation - A Co-occurrence-based Framework. Multimedia Systems, 2013.

Digital Library

[18]

A. Swartz. Musicbrainz: A semantic web service. Intelligent Systems, IEEE, 17(1):76--77, 2002.

Digital Library

[19]

Twitter API. https://dev.twitter.com/docs/api/1.1/post/statuses/filter.

[20]

S. Weibel, J. Kunze, C. Lagoze, and M. Wolf. Dublin Core Metadata for Resource Discovery. Internet Engineering Task Force RFC, 2413(222):132, 1998.

Digital Library

[21]

What is Hashtag Hijacking? http://smallbiztrends.com/2013/08/what-is-hashtag-hijacking-2.html.

[22]

E. Zangerle, W. Gassler, and G. Specht. Using Tag Recommendations to Homogenize Folksonomies in Microblogging Environments. In Proc. of the Third Intl. Conference on Social Informatics (SocInfo) 2011, Singapore., Lecture Notes in Computer Science, pages 113--126. Springer, 2011.

Digital Library

[23]

E. Zangerle, W. Gassler, and G. Specht. Exploiting Twitter's Collective Knowledge for Music Recommendations. In Proc. WWW Workshop: Making Sense of Microposts MSM, 2012.

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Index Terms

#nowplaying Music Dataset: Extracting Listening Behavior from Twitter

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cover image ACM Conferences

WISMM '14: Proceedings of the First International Workshop on Internet-Scale Multimedia Management

November 2014

74 pages

ISBN:9781450331579

DOI:10.1145/2661714

General Chairs:
Roger Zimmermann
National University of Singapore, Singapore
,
Yi Yu
National University of Singapore, Singapore

Copyright © 2014 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].

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Published: 07 November 2014

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University of Innsbruck (Forschungsfürderungsmittel der Nachwuchsfürderung 2013 der Universität Innsbruck)

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MM '14

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MM '14: 2014 ACM Multimedia Conference

November 7, 2014

Florida, Orlando, USA

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WISMM '14 Paper Acceptance Rate 6 of 18 submissions, 33%;

Overall Acceptance Rate 6 of 18 submissions, 33%

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The 32nd ACM International Conference on Multimedia

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https://doi.org/10.1016/j.eswa.2024.125273
Wang ZWei WFeng SMao XQiu MChen DFang R(2024)Exploiting Group-Level Behavior Pattern for Session-Based RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328031036:1(152-166)Online publication date: Jan-2024
https://doi.org/10.1109/TKDE.2023.3280310
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https://doi.org/10.1016/j.patcog.2023.109911
Huang LLi RLei JJi YFeng GShi WYang S(2024)Sequence-Aware Graph Neural Network Incorporating Neighborhood Information for Session-Based RecommendationInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00408-917:1Online publication date: 14-Feb-2024
https://doi.org/10.1007/s44196-024-00408-9
Gao SWang JZeng YDong X(2024)TGIE4REC: enhancing session-based recommendation with transition and global informationThe Journal of Supercomputing10.1007/s11227-024-05897-180:8(11585-11613)Online publication date: 29-Jan-2024
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Bendada WSalha-Galvan GBouabça TCazenave TChen HDuh WHuang HKato MMothe JPoblete B(2023)A Scalable Framework for Automatic Playlist Continuation on Music Streaming ServicesProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591628(464-474)Online publication date: 19-Jul-2023
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