A Two-stage Prediction Method of News Popularity only using Content Features

Li, Yiding; Peng, Qinke; Sun, Zhoutong; Fu, Laiyi; Khokhar, Salah-ud-din

doi:10.1109/wcica.2018.8630557

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…The technique extends gradient boosting models to predict the number of shares using an ensemble of learning algorithms. • Vector space model (VSM) proposed in [13], which applies a two-stage selection approach to predict news popularity. The method selects global features related to column information and then chooses local features related to news popularity.…”

Section: Experiments Settingsmentioning

confidence: 99%

“…An extensive set of experiments are performed to evaluate the proposed scheme and compare it with three state-of-the-art techniques. The comparing approaches include an ensemble model [8], a vector space model [13], a gradient boosting learning approach [14] along with traditional active learning strategies to predict social content popularity [15]. The experimental evaluation aims to estimate the proposed model's effectiveness in popularity predictions with different classification models and a set of datasets with various sizes and dimensionality.…”

Section: Introductionmentioning

confidence: 99%

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

Nashaat¹,

Miller²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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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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Section: Experiments Settingsmentioning

confidence: 99%