Improving News Popularity Estimation via Weak Supervision and Meta-active Learning
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Date
2021-01-05
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2679
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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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10 pages
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Proceedings of the 54th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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