Portraying emotion and trustworthiness is known to increase the appeal of video content. However, the causal relationship between these signals and online user engagement is not well understood. This limited understanding is partly due to a scarcity in emotionally annotated data and the varied modalities which express user engagement online. In this contribution, we utilise a large dataset of YouTube review videos which includes ca. 600 hours of dimensional arousal, valence and trustworthiness annotations. We investigate features extracted from these signals against various user engagement indicators including views, like/dislike ratio, as well as the sentiment of comments. In doing so, we identify the positive and negative influences which single features have, as well as interpretable patterns in each dimension which relate to user engagement. Our results demonstrate that smaller boundary ranges and fluctuations for arousal lead to an increase in user engagement. Furthermore, the extracted time-series features reveal significant (p<0.05) correlations for each dimension, such as, count below signal mean (arousal), number of peaks (valence), and absolute energy (trustworthiness). From this, an effective combination of features is outlined for approaches aiming to automatically predict several user engagement indicators. In a user engagement prediction paradigm we compare all features against semi-automatic (cross-task), and automatic (task-specific) feature selection methods. These selected feature sets appear to outperform the usage of all features, e.g., using all features achieves 1.55 likes per day (Lp/d) mean absolute error from valence; this improves through semi-automatic and automatic selection to 1.33 and 1.23 Lp/d, respectively (data mean 9.72 Lp/d with a std. 28.75 Lp/d).

中文翻译：

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从持续情感特征估算在线视频用户参与度

描绘情感和可信度可以提高视频内容的吸引力。但是，这些信号与在线用户参与之间的因果关系尚未得到很好的理解。这种有限的理解部分是由于情感注释数据的匮乏以及表达用户在线参与度的各种方式所致。在这项贡献中，我们利用了大量的YouTube评论视频数据集，其中包括大约 600小时的尺寸唤醒，效价和可信赖性注释。我们针对各种用户参与度指标调查了从这些信号中提取的功能，这些指标包括视图，喜欢/不喜欢率以及评论的情绪。通过这样做，我们确定了单个功能所具有的正面和负面影响，以及每个维度中与用户参与有关的可解释模式。我们的结果表明，较小的边界范围和引起的波动会导致用户参与度的增加。此外，提取的时间序列特征揭示了每个维度的显着（p <0.05）相关性，例如，低于信号平均值（计数）的计数，峰值数量（价）和绝对能量（可信度）。由此，针对旨在自动预测多个用户参与度指标的方法，概述了功能的有效组合。在用户参与度预测范例中，我们将所有功能与半自动（跨任务）和自动（特定于任务）功能选择方法进行比较。这些选定的功能集似乎胜过所有功能的使用，例如，使用所有功能均实现每日1.55个赞（Lp / d）表示平均无效价。