Mining microblogs to infer music artist similarity and cultural listening patterns

Schedl, Markus; Hauger, David

doi:10.1145/2187980.2188218

Cited by 17 publications

(17 citation statements)

References 32 publications

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“…The #itunes hashtag represents users of iTunes for the most part, while the #nowplaying hashtag is more general and therefore represents a wider population of users. For a comparison of listening habits expressed by #itunes and by #nowplaying, the interested reader is referred to [33].…”

Section: Data Acquisition and Analysismentioning

confidence: 99%

Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

2013

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Microtexts are a valuable, albeit noisy, source to infer collaborative information. As music plays an important role in many human lives, microblogs on musicrelated activities are available in abundance. This paper investigates different strategies to estimate music similarity from these data sources. In particular, we first present a framework to extract co-occurrence scores between music artists from microblogs and then investigate 12 similarity estimation functions to subsequently derive resemblance scores. We evaluate the approaches on a collection of microblogs crawled from Twitter over a period of 10 months and compare them to standard tf-idf approaches. As evaluation criteria we use precision and recall in an artist retrieval task as well as rank proximity. We show that collaborative chatter on music can be effectively used to develop music artist similarity measures, which are a core part of every music retrieval and recommendation system. Furthermore, we analyze the effects of the ''long tail'' on retrieval results and investigate whether results are consistent over time, using a second dataset.

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Section: Data Acquisition and Analysismentioning

confidence: 99%

Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

2013

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“…Quantitative analyses of listening events posted on social media are carried out by Schedl and Hauger in [8]. They look into geolocated musical tweets and aggregate them according to country and city.…”

Section: Related Workmentioning

confidence: 99%

Genre-based Analysis of Social Media Data on Music Listening Behavior

Schedl

Tkalčič

2014

Proceedings of the First International Workshop on Internet-Scale Multimedia Management

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It is frequently presumed that lovers of Classical music are not present in social media. In this paper, we investigate whether this statement can be empirically verified. To this end, we compare two social media platforms -Last.fm and Twitter -and perform a study on musical preference of their respective users. We investigate two research hypotheses: (i) Classical music fan are more reluctant to use social media to indicate their listing habits than listeners of other genres and (ii) there are correlations between the use of Last.fm and Twitter to indicate music listening behavior. Both hypotheses are verified and substantial differences could be made out for Twitter users. The results of these investigations will help improve music recommendation systems for listeners with non-mainstream music taste.

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“…The authors propose a co-occurrence-based approach to construct a song recommender system. Schedl and Hauger [28] use microblog data from all cities with more than 500,000 inhabitants in order to calculate deviations of musical taste from the mainstream on country and city level.…”

Section: Mining Microblog Datamentioning

confidence: 99%

“…For instance, the tweet "#np LenaSatellite" matches the patterns "artist name -song title" and "song title -artist name", with both "Lena" and "Satellite" being valid potential artist names [28].…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

Exploring Geospatial Music Listening Patterns in Microblog Data

Hauger

Schedl

2014

Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation

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Abstract. Microblogs are a steadily growing, valuable, albeit noisy, source of information on interests, preferences, and activities. As music plays an important role in many human lives we aim to leverage microblogs for music listening-related information. Based on this information we present approaches to estimate artist similarity, popularity, and local trends, as well as approaches to cluster artists with respect to additional tag information. Furthermore, we elaborate a novel geo-aware interaction approach that integrates these diverse pieces of information mined from music-related tweets. Including geospatial information at the level of tweets, we also present a web-based user interface to browse the "world of music" as seen by the "Twittersphere".

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Mining microblogs to infer music artist similarity and cultural listening patterns

Cited by 17 publications

References 32 publications

Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

Harvesting microblogs for contextual music similarity estimation: a co-occurrence-based framework

Genre-based Analysis of Social Media Data on Music Listening Behavior

Exploring Geospatial Music Listening Patterns in Microblog Data

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