Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70941

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

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Item Summary

Title:Improving News Popularity Estimation via Weak Supervision and Meta-active Learning
Authors:Nashaat, Mona
Miller, James
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
classification
machine learning
popularity prediction
social media
Date Issued:05 Jan 2021
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%.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/70941
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.327
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


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