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http://hdl.handle.net/10125/70941
Improving News Popularity Estimation via Weak Supervision and Meta-active Learning
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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