An Iterative Refinement Approach for Social Media Headline Prediction
This work addresses the challenge of accurately forecasting view counts or popularity for social media content, which is important for content creators and platforms, but it appears incremental as it builds on existing ensemble methods.
The paper tackles the problem of predicting extreme popularity scores in social media by proposing an iterative refinement approach that first predicts initial scores and then compensates residues using an ensemble regressor, resulting in outperforming state-of-the-art regression methods.
In this study, we propose a novel iterative refinement approach to predict the popularity score of the social media meta-data effectively. With the rapid growth of the social media on the Internet, how to adequately forecast the view count or popularity becomes more important. Conventionally, the ensemble approach such as random forest regression achieves high and stable performance on various prediction tasks. However, most of the regression methods may not precisely predict the extreme high or low values. To address this issue, we first predict the initial popularity score and retrieve their residues. In order to correctly compensate those extreme values, we adopt an ensemble regressor to compensate the residues to further improve the prediction performance. Comprehensive experiments are conducted to demonstrate the proposed iterative refinement approach outperforms the state-of-the-art regression approach.