Predicting the popularity of web content is widely regarded as an important but challenging task. Online news articles are typical examples of this. In particular, owing to their time-sensitive nature, it is preferable to predict the popularity of news articles before their publication. To achieve this, this study proposes a named entity topic model (NETM) to extract the textual factors that can drive popularity growth. Here, each named entity is assumed to have a popularity-gain distribution over all semantic topics. The popularity of a news article is considered as the accumulation of popularity gains generated by its named entities (NEs) over all the topics. By learning the popularity-gain matrix for each named entity, the popularity of any news article can be predicted. Experiments on two collections of news articles demonstrate that the proposed NETM can outperform existing models in terms of accuracy. Additionally, the popularity-gain matrix learned by the NETM can be used to effectively explain the popularity of specific news articles.

预测Web内容的流行程度被广泛认为是一项重要但具有挑战性的任务。在线新闻文章就是典型的例子。特别是，由于它们具有时间敏感性，因此最好在新闻发布之前预测新闻的流行程度。为了实现这一目标，本研究提出了一个命名实体主题模型（NETM），以提取可以推动人气增长的文字因素。在此，假定每个命名实体在所有语义主题上都具有受欢迎度-收益分布。新闻文章的受欢迎程度被认为是其命名实体（NE）在所有主题上产生的受欢迎程度累积的结果。通过学习每个命名实体的流行度-增益矩阵，可以预测任何新闻文章的流行度。对两套新闻报道的实验表明，所提出的NETM在准确性方面可以胜过现有模型。此外，由NETM学习的受欢迎度增益矩阵可用于有效解释特定新闻文章的受欢迎程度。