Although affective expressions of individuals have been extensively studied
using social media, research has primarily focused on the Western context.
There are substantial differences among cultures that contribute to their
affective expressions. This paper examines the differences between Twitter (X)
in the United States and Sina Weibo posts in China on two primary dimensions of
affect - valence and arousal. We study the difference in the functional
relationship between arousal and valence (so-called V-shaped) among individuals
in the US and China and explore the associated content differences.
Furthermore, we correlate word usage and topics in both platforms to interpret
their differences. We observe that for Twitter users, the variation in
emotional intensity is less distinct between negative and positive emotions
compared to Weibo users, and there is a sharper escalation in arousal
corresponding with heightened emotions. From language features, we discover
that affective expressions are associated with personal life and feelings on
Twitter, while on Weibo such discussions are about socio-political topics in
the society. These results suggest a West-East difference in the V-shaped
relationship between valence and arousal of affective expressions on social
media influenced by content differences. Our findings have implications for
applications and theories related to cultural differences in affective
expressions.

通过对美国的 Twitter 和中国的新浪微博的比较研究，本文探讨了不同社交媒体平台上用户情感（两个主要维度为情感价值和情感唤起度）之间的差异，同时也研究了其关联的内容差异，并发现来自不同文化背景的用户对情感的表达方式存在明显差异，并从语言特征上解释了这些差异。

美国和中国之间的语言基于的情绪价值和唤起表达：跨文化研究

Language-based Valence and Arousal Expressions between the United States  and China: a Cross-Cultural Examination

Decades of research indicate that emotion recognition is more effective when
drawing information from multiple modalities. But what if some modalities are
sometimes missing? To address this problem, we propose a novel
Transformer-based architecture for recognizing valence and arousal in a
time-continuous manner even with missing input modalities. We use a coupling of
cross-attention and self-attention mechanisms to emphasize relationships
between modalities during time and enhance the learning process on weak salient
inputs. Experimental results on the Ulm-TSST dataset show that our model
exhibits an improvement of the concordance correlation coefficient evaluation
of 37% when predicting arousal values and 30% when predicting valence values,
compared to a late-fusion baseline approach.

提出了一种基于 Transformer 的架构，用于识别情绪的正性和负性，即使输入模态缺失，该模型通过交叉关注和自关注机制强调模态之间的关系，提高了对弱显著输入的学习过程，并在 Ul-TSST 数据集上实验，与后期融合基准方法相比，预测唤醒值的一致性相关系数的评估提高了 37％，预测价值的一致性相关系数的评估提高了 30%。

在时间连续的多模态情感识别中容纳缺失的模态

Accommodating Missing Modalities in Time-Continuous Multimodal Emotion  Recognition

As 3D facial avatars become more widely used for communication, it is
critical that they faithfully convey emotion. Unfortunately, the best recent
methods that regress parametric 3D face models from monocular images are unable
to capture the full spectrum of facial expression, such as subtle or extreme
emotions. We find the standard reconstruction metrics used for training
(landmark reprojection error, photometric error, and face recognition loss) are
insufficient to capture high-fidelity expressions. The result is facial
geometries that do not match the emotional content of the input image. We
address this with EMOCA (EMOtion Capture and Animation), by introducing a novel
deep perceptual emotion consistency loss during training, which helps ensure
that the reconstructed 3D expression matches the expression depicted in the
input image. While EMOCA achieves 3D reconstruction errors that are on par with
the current best methods, it significantly outperforms them in terms of the
quality of the reconstructed expression and the perceived emotional content. We
also directly regress levels of valence and arousal and classify basic
expressions from the estimated 3D face parameters. On the task of in-the-wild
emotion recognition, our purely geometric approach is on par with the best
image-based methods, highlighting the value of 3D geometry in analyzing human
behavior. The model and code are publicly available at
this https URL

研究使用 3D 面部化身进行情感传达，提出了一种新的深度知觉情感一致性丢失训练方法 EMOCA，实现了高保真的面部表情重建和情感传达，可用于分析人类行为。