Predicting Talent Breakout Rate using Twitter and TV data
This work addresses the need for early talent detection in advertising, but it is incremental as it applies existing methods to a new domain-specific dataset.
The paper tackled the problem of predicting talent breakout rates by combining Twitter and TV data to detect Japanese talents before they become famous, finding that neural networks outperformed traditional and ensemble methods in precision and recall for forecasting ability.
Early detection of rising talents is of paramount importance in the field of advertising. In this paper, we define a concept of talent breakout and propose a method to detect Japanese talents before their rise to stardom. The main focus of the study is to determine the effectiveness of combining Twitter and TV data on predicting time-dependent changes in social data. Although traditional time-series models are known to be robust in many applications, the success of neural network models in various fields (e.g.\ Natural Language Processing, Computer Vision, Reinforcement Learning) continues to spark an interest in the time-series community to apply new techniques in practice. Therefore, in order to find the best modeling approach, we have experimented with traditional, neural network and ensemble learning methods. We observe that ensemble learning methods outperform traditional and neural network models based on standard regression metrics. However, by utilizing the concept of talent breakout, we are able to assess the true forecasting ability of the models, where neural networks outperform traditional and ensemble learning methods in terms of precision and recall.