research-article

#nowplaying the future billboard: mining music listening behaviors of twitter users for hit song prediction

Authors:

Yekyung Kim,

Bongwon Suh,

Kyogu LeeAuthors Info & Claims

SoMeRA '14: Proceedings of the first international workshop on Social media retrieval and analysis

Pages 51 - 56

https://doi.org/10.1145/2632188.2632206

Published: 11 July 2014 Publication History

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Abstract

Microblogs are rich sources of information because they provide platforms for users to share their thoughts, news, information, activities, and so on. Twitter is one of the most popular microblogs. Twitter users often use hashtags to mark specific topics and to link them with related tweets. In this study, we investigate the relationship between the music listening behaviors of Twitter users and a popular music ranking service by comparing information extracted from tweets with music-related hashtags and the Billboard chart. We collect users' music listening behavior from Twitter using music-related hashtags (e.g., #nowplaying). We then build a predictive model to forecast the Billboard rankings and hit music. The results show that the numbers of daily tweets about a specific song and artist can be effectively used to predict Billboard rankings and hits. This research suggests that users' music listening behavior on Twitter is highly correlated with general music trends and could play an important role in understanding consumers' music consumption patterns. In addition, we believe that Twitter users' music listening behavior can be applied in the field of Music Information Retrieval (MIR).

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Cited By

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N KP SAsib M MS K(2024)A Comprehensive Approach to Song Popularity Forecasting2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)10.1109/AIMLA59606.2024.10531405(1-5)Online publication date: 15-Mar-2024
https://doi.org/10.1109/AIMLA59606.2024.10531405
Reisz NServedio VThurner S(2024)Quantifying the impact of homophily and influencer networks on song popularity predictionScientific Reports10.1038/s41598-024-58969-w14:1Online publication date: 18-Apr-2024
https://doi.org/10.1038/s41598-024-58969-w
Vardo LJerkić JŽunić E(2023)Predicting Song Success: Understanding Track Features and Predicting Popularity Using Spotify Data2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH)10.1109/INFOTEH57020.2023.10094172(1-6)Online publication date: 15-Mar-2023
https://doi.org/10.1109/INFOTEH57020.2023.10094172
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Index Terms

#nowplaying the future billboard: mining music listening behaviors of twitter users for hit song prediction
1. Information systems
  1. Information retrieval

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Published In

SoMeRA '14: Proceedings of the first international workshop on Social media retrieval and analysis

July 2014

72 pages

ISBN:9781450330220

DOI:10.1145/2632188

Program Chairs:
Markus Schedl
Johannes Kepler University Linz, Austria
,
Peter Knees
Johannes Kepler University Linz, Austria
,
Jialie Shen
Singapore Management University, Singapore

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2014

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Funding Sources

Seoul National University

Conference

SIGIR '14

Sponsor:

SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11, 2014

Queensland, Gold Coast, Australia

Acceptance Rates

SoMeRA '14 Paper Acceptance Rate 13 of 19 submissions, 68%;

Overall Acceptance Rate 13 of 19 submissions, 68%

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Cited By

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N KP SAsib M MS K(2024)A Comprehensive Approach to Song Popularity Forecasting2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)10.1109/AIMLA59606.2024.10531405(1-5)Online publication date: 15-Mar-2024
https://doi.org/10.1109/AIMLA59606.2024.10531405
Reisz NServedio VThurner S(2024)Quantifying the impact of homophily and influencer networks on song popularity predictionScientific Reports10.1038/s41598-024-58969-w14:1Online publication date: 18-Apr-2024
https://doi.org/10.1038/s41598-024-58969-w
Vardo LJerkić JŽunić E(2023)Predicting Song Success: Understanding Track Features and Predicting Popularity Using Spotify Data2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH)10.1109/INFOTEH57020.2023.10094172(1-6)Online publication date: 15-Mar-2023
https://doi.org/10.1109/INFOTEH57020.2023.10094172
Seufitelli DOliveira GSilva MScofield CMoro M(2023)Hit song science: a comprehensive survey and research directionsJournal of New Music Research10.1080/09298215.2023.228299952:1(41-72)Online publication date: 20-Nov-2023
https://doi.org/10.1080/09298215.2023.2282999
Zeng HYuan QGuo LXu S(2022)Song popularity prediction model based on multi-modal feature fusion and LightGBMProceedings of the 8th International Conference on Communication and Information Processing10.1145/3571662.3571667(28-32)Online publication date: 3-Nov-2022
https://dl.acm.org/doi/10.1145/3571662.3571667
Silva MOliveira GSeufitelli DLacerda AMoro M(2022)Collaboration as a Driving Factor for Hit Song ClassificationProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3556993(66-74)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3539637.3556993
Aum JKim JPark E(2022)Can we predict the Billboard music chart winner? Machine learning prediction based on Twitter artist-fan interactionsBehaviour & Information Technology10.1080/0144929X.2022.204273742:6(775-788)Online publication date: 27-Feb-2022
https://doi.org/10.1080/0144929X.2022.2042737
Lee MKim H(2022)Exploring determinants of digital music success in South KoreaElectronic Commerce Research10.1007/s10660-022-09573-5Online publication date: 14-Jun-2022
https://doi.org/10.1007/s10660-022-09573-5
Rosati DWoolhouse MBolker BEarn D(2021)Modelling song popularity asacontagious processProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences10.1098/rspa.2021.0457477:2253(20210457)Online publication date: 22-Sep-2021
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Koutlis CSchinas MPapadopoulos SKompatsiaris I(2020)GAP: Geometric Aggregation of Popularity MetricsInformation10.3390/info1106032311:6(323)Online publication date: 15-Jun-2020
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