{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/discovering-patterns-of-online-popularity","title":"Discovering patterns of online popularity from time series","arxiv_id":"1904.04994","date":"2019-04-10","proceeding":null,"authors":["Mert Ozer","Anna Sapienza","Andrés Abeliuk","Goran Muric","Emilio Ferrara"],"abstract":"How is popularity gained online? Is being successful strictly related to\nrapidly becoming viral in an online platform or is it possible to acquire\npopularity in a steady and disciplined fashion? What are other temporal\ncharacteristics that can unveil the popularity of online content? To answer\nthese questions, we leverage a multi-faceted temporal analysis of the evolution\nof popular online contents. Here, we present dipm-SC: a multi-dimensional\nshape-based time-series clustering algorithm with a heuristic to find the\noptimal number of clusters. First, we validate the accuracy of our algorithm on\nsynthetic datasets generated from benchmark time series models. Second, we show\nthat dipm-SC can uncover meaningful clusters of popularity behaviors in a\nreal-world Twitter dataset. By clustering the multidimensional time-series of\nthe popularity of contents coupled with other domain-specific dimensions, we\nuncover two main patterns of popularity: bursty and steady temporal behaviors.\nMoreover, we find that the way popularity is gained over time has no\nsignificant impact on the final cumulative popularity.","url_abs":"http://arxiv.org/abs/1904.04994v1","url_pdf":"http://arxiv.org/pdf/1904.04994v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"discovering-patterns-of-online-popularity","repo_url":"https://github.com/mertozer/mts-clustering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-clustering","task_name":"Time Series Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}