Predicting the success of online petitions leveraging multidimensional time-series

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• dc.contributor.author Proskurnia, Julia
• dc.contributor.author Grabowicz, Przemyslaw
• dc.contributor.author Kobayashi, Ryota
• dc.contributor.author Castillo, Carlos
• dc.contributor.author Cudré-Mauroux, Philippe
• dc.contributor.author Aberer, Karl
• dc.date.accessioned 2019-03-20T18:46:27Z
• dc.date.available 2019-03-20T18:46:27Z
• dc.date.issued 2017
• dc.description Comunicació presentada a: WWW '17 the 26th International Conference on World Wide Web, celebrada del 3 al 7 d'abril de 2017 a Perth, Austràlia.ca
• dc.description.abstract Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolu- tion of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site|one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.
• dc.description.sponsorship This work was supported by the Catalonia Trade and Investment Agency (Agència per la competitivitat de l'empresa, ACCIÓ); ACT-I, JST, JSPS KAKENHI Grant Number 25870915, and the Okawa Foundation for Information and Telecommunications.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Proskurnia J, Grabowicz PA, Kobayashi R, Castillo C, Cudré-Mauroux P, Aberer K. Predicting the success of online petitions leveraging multidimensional time-series. In: WWW '17 Proceedings of the 26th International Conference on World Wide Web; 2017 Apr 3-7; Perth, Australia. New York: ACM; 2017. p. 755-64. DOI: 10.1145/3038912.3052705
• dc.identifier.doi http://dx.doi.org/10.1145/3038912.3052705
• dc.identifier.isbn 978-1-4503-4913-0
• dc.identifier.uri http://hdl.handle.net/10230/36875
• dc.language.iso eng
• dc.publisher ACM Association for Computer Machinery
• dc.relation.ispartof WWW '17 Proceedings of the 26th International Conference on World Wide Web; 2017 Apr 3-7; Perth, Australia. New York: ACM; 2017. p. 755-64.
• dc.rights © 2017 International World Wide Web Conference Committee (IW3C2),published under Creative Commons CC BY 4.0 License
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Web applicationsen
• dc.subject.keyword Online petitionsen
• dc.subject.keyword Time series predictionen
• dc.title Predicting the success of online petitions leveraging multidimensional time-series
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion