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Improving News Popularity Estimation via Weak Supervision and Meta-active Learning

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dc.contributor.author Nashaat, Mona
dc.contributor.author Miller, James
dc.date.accessioned 2020-12-24T19:32:44Z
dc.date.available 2020-12-24T19:32:44Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70941
dc.description.abstract Social news has fundamentally changed the mechanisms of public perception, education, and even dis-information. Apprising the popularity of social news articles can have significant impact through a diversity of information redistribution techniques. In this article, an improved prediction algorithm is proposed to predict the long-time popularity of social news articles without the need for ground-truth observations. The proposed framework applies a novel active learning selection policy to obtain the optimal volume of observations and achieve superior predictive performance. To assess the proposed framework, a large set of experiments are undertaken; these indicate that the new solution can improve prediction performance by 28% (precision) while reducing the volume of required ground truth by 32%.
dc.format.extent 10 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining and Machine Learning for Social Media
dc.subject classification
dc.subject machine learning
dc.subject popularity prediction
dc.subject social media
dc.title Improving News Popularity Estimation via Weak Supervision and Meta-active Learning
dc.identifier.doi 10.24251/HICSS.2021.327
prism.startingpage 2679
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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