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

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dc.contributor.authorNashaat, Mona
dc.contributor.authorMiller, James
dc.date.accessioned2020-12-24T19:32:44Z
dc.date.available2020-12-24T19:32:44Z
dc.date.issued2021-01-05
dc.description.abstractSocial 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2021.327
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70941
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectclassification
dc.subjectmachine learning
dc.subjectpopularity prediction
dc.subjectsocial media
dc.titleImproving News Popularity Estimation via Weak Supervision and Meta-active Learning
prism.startingpage2679

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