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

Date
2021-01-05
Authors
Nashaat, Mona
Miller, James
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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%.
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Data Analytics, Data Mining and Machine Learning for Social Media, classification, machine learning, popularity prediction, social media
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