In this paper, we propose a non-intrusive and nonrestrictive multimodal deep learning model for estimating the engagement levels of game streamers. We incorporate three modalities from the streamers' videos (facial, pixel, and audio information) to train the multimodal neural network. Additionally, we introduce a novel interpretation technique that directly calculates the contribution of each modality to the model's classification performance without the need to retrain single modality models. Experimental results demonstrate that our model achieves an accuracy of 77.2% on the test set, with the sound modality identified as a key modality for engagement estimation. By utilizing the proposed interpretation technique, we further analyze the modality contributions of the model in handling different categories and samples from various players. This enhances the model's interpretability and reveals its limitations, as well as future directions for improvement. The proposed approach and findings have potential applications in the fields of game streaming and audience analysis, as well as in domains related to multimodal learning and affective computing.

Souls-like games are one of the most popular and emerging genres in the contemporary gaming world. This study compared the behavioral characteristics, perspectives, and emotional expressions of players in Souls-like games from different cultural backgrounds, specifically examining the distinctions and commonalities among them. Natural language processing techniques were employed to analyze English, Chinese, and Russian reviews of 17 Souls-like games to investigate players' gaming experiences, including gameplay behaviors, game evaluations, and emotional experiences. The findings revealed significant disparities among players from different cultures in all three aspects of their engagement with Souls-like games. Specifically, these players exhibited significant culture-related variations in their behavioral characteristics towards Souls-like games. In terms of perspectives, English-speaking players tended to focus more on game optimization, whereas Chinese and Russian players paid greater attention to game combat design. Regarding emotional expressions, Chinese players were more prone to exhibit emotions of anger and disgust, while English and Russian players displayed a more neutral emotional stance. These cultural insights provide valuable information for game developers to better meet the needs and expectations of players from different cultural backgrounds. This study not only broadens our understanding of player behaviors and cultural influences but also lends robust support to cross-cultural gaming research.