{"ID":2871503,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00006","arxiv_id":"2510.00006","title":"Unpacking Musical Symbolism in Online Communities: Content-Based and Network-Centric Approaches","abstract":"This paper examines how musical symbolism is produced and circulated in online communities by combining content-based music analysis with a lightweight network perspective on lyrics. Using a curated corpus of 275 chart-topping songs enriched with audio descriptors (energy, danceability, loudness, liveness, valence, acousticness, speechiness, popularity) and full lyric transcripts, we build a reproducible pipeline that (i) quantifies temporal trends in sonic attributes, (ii) models lexical salience and co-occurrence, and (iii) profiles mood by genre. We find a decade-long decline in energy (79 -\u003e 58) alongside a rise in danceability (59 -\u003e 73); valence peaks in 2013 (63) and dips in 2014-2016 (42) before partially recovering. Correlation analysis shows strong coupling of energy with loudness (r = 0.74) and negative associations for acousticness with both energy (r = -0.54) and loudness (r = -0.51); danceability is largely orthogonal to other features (|r| \u003c 0.20). Lyric tokenization (\u003e114k tokens) reveals a pronoun-centric lexicon \"I/you/me/my\" and a dense co-occurrence structure in which interpersonal address anchors mainstream narratives. Mood differs systematically by style: R\u0026B exhibits the highest mean valence (96), followed by K-Pop/Pop (77) and Indie/Pop (70), whereas Latin/Reggaeton is lower (37) despite high danceability. Read through a subcultural identity lens, these patterns suggest the mainstreaming of previously peripheral codes and a commercial preference for relaxed yet rhythmically engaging productions that sustain collective participation without maximal intensity. Methodologically, we contribute an integrated MIR-plus-network workflow spanning summary statistics, correlation structure, lexical co-occurrence matrices, and genre-wise mood profiling that is robust to modality sparsity and suitable for socially aware recommendation or community-level diffusion studies.","short_abstract":"This paper examines how musical symbolism is produced and circulated in online communities by combining content-based music analysis with a lightweight network perspective on lyrics. Using a curated corpus of 275 chart-topping songs enriched with audio descriptors (energy, danceability, loudness, liveness, valence, aco...","url_abs":"https://arxiv.org/abs/2510.00006","url_pdf":"https://arxiv.org/pdf/2510.00006v1","authors":"[\"Kajwan Ziaoddini\"]","published":"2025-09-13T02:15:02Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.CL\",\"cs.CY\",\"cs.MM\",\"eess.AS\"]","methods":"[\"Diffusion Model\"]","has_code":false}