用推特和电视数据预测日本艺人走红概率，神经网络更准。
Predicting Talent Breakout Rate using Twitter and TV data
- 融合推特热度与电视曝光数据，构建预测模型。
- 神经网络在精准率和召回率上优于传统与集成方法。
- 适合关注明星潜力挖掘的广告与娱乐行业从业者。
在广告领域，早期发现潜在明星至关重要。本文定义了‘艺人走红’的概念，并提出一种方法，在日本艺人声名鹊起前进行预测。研究重点在于评估结合推特与电视数据对社会数据动态变化的预测效果。尽管传统时间序列模型在诸多应用中表现稳健，但神经网络在自然语言处理、计算机视觉、强化学习等领域的成功，促使时间序列领域探索新方法。为此，我们对比了传统模型、神经网络及集成学习方法。结果表明，基于标准回归指标，集成学习表现最佳；但引入‘走红’概念后，神经网络在精度与召回率上显著优于传统与集成方法。
原文摘要 · Abstract (English)
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.
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