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Título:
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Predicting the success of online petitions leveraging multidimensional time-series
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Autor/a:
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Proskurnia, Julia; Grabowicz, Przemyslaw; Kobayashi, Ryota; Castillo, Carlos; Cudré-Mauroux, Philippe; Aberer, Karl
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Abstract:
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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. |
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Abstract:
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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. |
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Abstract:
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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. |
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Materia(s):
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-Web applications
-Online petitions
-Time series prediction |
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Derechos:
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© 2017 International World Wide Web Conference Committee (IW3C2),published under Creative Commons CC BY 4.0 License
https://creativecommons.org/licenses/by/4.0/
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Tipo de documento:
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Objeto de conferencia
Artículo - Versión publicada |
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Editor:
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ACM Association for Computer Machinery
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