2013
DOI: 10.1007/978-3-642-36973-5_87
|View full text |Cite
|
Sign up to set email alerts
|

Leveraging Microblogs for Spatiotemporal Music Information Retrieval

Abstract: Abstract. We present results of text data mining experiments for music retrieval, analyzing microblogs gathered from November 2011 to September 2012 to infer music listening patterns all around the world. We assess relationships between particular music preferences and spatial properties, such as month, weekday, and country, and the temporal stability of listening activities. The findings of our study will help improve music retrieval and recommendation systems in that it will allow to incorporate geospatial a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
24
0

Year Published

2013
2013
2021
2021

Publication Types

Select...
5
2
1

Relationship

4
4

Authors

Journals

citations
Cited by 24 publications
(24 citation statements)
references
References 7 publications
0
24
0
Order By: Relevance
“…Particularly focusing on social media music retrieval, Schedl proposes in [32] a standardized corpus of data on music listening behavior mined form microblogs. The paper further reports findings of correlation analyses investigating the spatial and temporal stability of the listening patterns.…”
Section: Related Workmentioning
confidence: 99%
“…Particularly focusing on social media music retrieval, Schedl proposes in [32] a standardized corpus of data on music listening behavior mined form microblogs. The paper further reports findings of correlation analyses investigating the spatial and temporal stability of the listening patterns.…”
Section: Related Workmentioning
confidence: 99%
“…More precisely, we investigate the correlation of the listening measure distributions between the two datasets, for the same genre (intra-genre, inter-dataset) and between all genres within each dataset 6 This figure is similar for Blues, Country, Jazz, Vocal, and World; but Electronic, Folk, HipHop, Metal, Pop, and Rock show smaller values for cumulative PC at top 20 artists (between 18% and 30%). 7 These cumulative PC values on the bottom 500 artists are similar for RnB and World; smaller (around 1%) for Blues, Country, EasyListening, and Vocal; and considerably higher for Electronic, Folk, HipHop, Jazz, Metal, Pop, Rap, and Rock (between 5% and 10%). 8 http://www.riaa.com/keystatistics.php?content_ selector=consumertrends 9 We are aware that the RIAA data only covers the USA, but given that the Last.fm community has a bias towards users from the US and that the Classical music share of Twitter users are even much lower, we are sure that the RIAA data does not underestimate the global share of Classical music in comparison to the social media data.…”
Section: Correlation Between Data Sources?mentioning
confidence: 88%
“…The authors then investigate particularities of music genre taste in different locations. In a subsequent work, Schedl categorizes music listening events posted on Twitter by mood tags, computes the global distribution of these mood tags, and investigates deviations from this global distribution on the country level [7].…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Music content and music context are incorporated using state-of-the-art feature extractors and corresponding similarity estimators. The user context is addressed by taking into account musical preference and geospatial data, using a standardized collection of listening behavior mined from microblog data [11]. We make use of the best feature extraction and similarity computation algorithms currently available to model music 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.…”
Section: Introductionmentioning
confidence: 99%