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#nowplaying the future billboard

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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-04117-9_19openalex-snapshot

Location-Aware Music Artist Recommendation

inferredcites →https://doi.org/10.1007/978-3-319-12093-5_7openalex-snapshot

Exploring Geospatial Music Listening Patterns in Microblog Data

inferredcites →https://doi.org/10.1007/978-3-642-03348-3_8openalex-snapshot

Social Knowledge-Driven Music Hit Prediction

inferredcites →https://doi.org/10.1007/978-3-642-36973-5_87openalex-snapshot

Leveraging Microblogs for Spatiotemporal Music Information Retrieval

inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot

Random Forests

inferredcites →https://doi.org/10.1023/b:stco.0000035301.49549.88openalex-snapshot

A tutorial on support vector regression

inferredcites →https://doi.org/10.1109/ism.2009.73openalex-snapshot

Predicting Billboard Success Using Data-Mining in P2P Networks

inferredcites →https://doi.org/10.1109/socialcom.2010.33openalex-snapshot

Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network

inferredcites →https://doi.org/10.1145/2187980.2188218openalex-snapshot

Mining microblogs to infer music artist similarity and cultural listening patterns

inferredcites →https://doi.org/10.5281/zenodo.1417648openalex-snapshot

The Million Musical Tweet Dataset - What We Can Learn From Microblogs.

inferredcites →https://doi.org/10.5281/zenodo.1417723openalex-snapshot

Hit Song Science Is Not Yet A Science.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.5281/zenodo.1417880openalex-extracted

Can Microblogs Predict Music Charts? An Analysis Of The Relationship Between #Nowplaying Tweets And Music Charts.

inferred← citeshttps://doi.org/10.5281/zenodo.3257096openalex-extracted

inferred← citeshttps://doi.org/10.5281/zenodo.3527808openalex-extracted

Hit Song Prediction: Leveraging Low- and High-Level Audio Features

inferred← citeshttps://doi.org/10.1007/978-3-030-49161-1_6openalex-snapshot

Using Twitter to Predict Chart Position for Songs

inferred← citeshttps://doi.org/10.1007/978-3-319-44944-9_50openalex-snapshot

Musical Track Popularity Mining Dataset

inferred← citeshttps://doi.org/10.1007/s10115-018-1319-2openalex-snapshot

Multi-agent system application for music features extraction, meta-classification and context analysis

inferred← citeshttps://doi.org/10.1007/s10660-022-09573-5openalex-snapshot

Exploring determinants of digital music success in South Korea

inferred← citeshttps://doi.org/10.1016/j.neucom.2017.09.100openalex-snapshot

Musical track popularity mining dataset: Extension & experimentation

inferred← citeshttps://doi.org/10.1026/02734-000openalex-snapshot

Is it the song and not the singer? Hit song prediction using structural features of melodies.

inferred← citeshttps://doi.org/10.1038/s41598-024-58969-wopenalex-snapshot

Quantifying the impact of homophily and influencer networks on song popularity prediction

inferred← citeshttps://doi.org/10.1080/0144929x.2022.2042737openalex-snapshot

Can we predict the Billboard music chart winner? Machine learning prediction based on Twitter artist-fan interactions

inferred← citeshttps://doi.org/10.1080/09298215.2023.2282999openalex-snapshot

Hit song science: a comprehensive survey and research directions

inferred← citeshttps://doi.org/10.1080/1369118x.2020.1761859openalex-snapshot

Platform pop: disentangling Spotify’s intermediary role in the music industry

inferred← citeshttps://doi.org/10.1098/rspa.2021.0457openalex-snapshot

Modelling song popularity as a contagious process

inferred← citeshttps://doi.org/10.1108/mip-04-2024-0209openalex-snapshot

Unveiling the melodic matrix: exploring genre-and-audio dynamics in the digital music popularity using machine learning techniques

inferred← citeshttps://doi.org/10.1109/aimla59606.2024.10531405openalex-snapshot

A Comprehensive Approach to Song Popularity Forecasting

inferred← citeshttps://doi.org/10.1109/databia50434.2020.9190613openalex-snapshot

Predicting a Hit Song with Machine Learning: Is there an apriori secret formula?

inferred← citeshttps://doi.org/10.1109/icmla.2019.00149openalex-snapshot

Predicting Music Popularity Using Music Charts

inferred← citeshttps://doi.org/10.1109/infoteh57020.2023.10094172openalex-snapshot

Predicting Song Success: Understanding Track Features and Predicting Popularity Using Spotify Data

inferred← citeshttps://doi.org/10.1145/2911451.2914679openalex-snapshot

GNMID14

inferred← citeshttps://doi.org/10.1145/3539637.3556993openalex-snapshot

Collaboration as a Driving Factor for Hit Song Classification

inferred← citeshttps://doi.org/10.1145/3571662.3571667openalex-snapshot

Song popularity prediction model based on multi-modal feature fusion and LightGBM

inferred← citeshttps://doi.org/10.1177/26339137251387144openalex-snapshot

Opinion leaders vs collective dynamics in cultural consumption: Decision-making in the choice of music

inferred← citeshttps://doi.org/10.22456/2175-2745.107021openalex-snapshot

A Model for Predicting Music Popularity on Streaming Platforms

inferred← citeshttps://doi.org/10.3389/fpsyg.2017.00416openalex-snapshot

Characterizing Listener Engagement with Popular Songs Using Large-Scale Music Discovery Data

inferred← citeshttps://doi.org/10.3390/info11060323openalex-snapshot

GAP: Geometric Aggregation of Popularity Metrics

inferred← citeshttps://doi.org/10.48550/arxiv.1710.10814openalex-snapshot

Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss

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