Analysis and Estimation of News Article Reading Time with Multimodal Machine Learning

This paper highlights the importance of reading time for news media and evaluates the implementation methodology. The display of estimated reading time allows users to select and view articles that are appropriate for their situation. The simplest hypothesis for the implementation is that reading time correlates with text length. We analyzed real-world users of Japanese financial news and revealed that reading time does not strongly correlate with text length. Experiments also showed that a multimodal machine learning approach leads to a more accurate estimation. Specifically, fine-tuning neural networks that incorporated LSTM to process user history and BERT and Swin Transformer to acquire embeddings from the articles achieved the best results.