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

Published: 11 July 2014 Publication History

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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  • (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
  • (2024)Unveiling the melodic matrix: exploring genre-and-audio dynamics in the digital music popularity using machine learning techniquesMarketing Intelligence & Planning10.1108/MIP-04-2024-020942:8(1333-1352)Online publication date: 26-Aug-2024
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    cover image ACM Conferences
    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,
    • Peter Knees,
    • Jialie Shen
    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: 11 July 2014

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    Author Tags

    1. data mining
    2. hit prediction
    3. machine learning

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

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    View all
    • (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
    • (2024)Unveiling the melodic matrix: exploring genre-and-audio dynamics in the digital music popularity using machine learning techniquesMarketing Intelligence & Planning10.1108/MIP-04-2024-020942:8(1333-1352)Online publication date: 26-Aug-2024
    • (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
    • (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
    • (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
    • (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
    • (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
    • (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
    • (2022)Exploring determinants of digital music success in South KoreaElectronic Commerce Research10.1007/s10660-022-09573-524:3(1659-1680)Online publication date: 14-Jun-2022
    • (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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