Purpose: The relationship between anchors and consumers in livestreaming environments is a significant area of research, with most prior studies focusing on anchor-specific characteristics. Based on emotional contagion and parasocial interaction theories, this study examines the relationship between anchor facial expressions and consumer purchasing behavior. Our research aims to identify a more widely applicable phenomenon and explain the mechanisms behind this relationship, contributing to theoretical development and offering practical insights into livestreaming platforms and anchors.Design/methodology/approach: Data were gathered from the Douyin livestreaming platform at varying traffic levels, with anchor facial expressions identified through an object detection model, resulting in a dataset of 22,406 entries. An ordinary least squares regression analysis and multiple robustness checks were performed to evaluate the proposed hypotheses.Findings: The results indicate that anchors’ positive facial expressions significantly enhance consumer purchasing behavior mediated by parasocial interaction and relationships. Notably, anchor influence negatively moderates this effect; the impact of facial expressions on consumer behavior is weaker for highly influential anchors and stronger for less influential ones.Originality/value: This study optimizes and categorizes facial expression classifications that adapt to livestreaming contexts, examines how anchors’ positive emotional facial expressions promote consumer purchasing behavior and explores the underlying mechanisms. This expands the research perspective on the role of visual cues in livestreaming and investigates the application of object detection models in this field, offering valuable insights from both theoretical and practical perspectives.

Nationwide reading has been promoted in China for a long time and is deeply rooted in Chinese culture. However, a significant gap persists between the initiatives for reading promotion and the actual demands of readers. This study conducts a comprehensive investigation of the current reading culture in China and explores differentiated approaches to reading promotion. Initially, a multi-source master booklist comprising 435 titles is compiled from mainstream platforms related to reading, including Douban Reading, Dangdang, and Ximalaya. Following the master booklist, 395,132 digital footprints of readers across 34 province-level and 354 prefecture-level regions in China are collected, spanning 2005 to 2023. Subsequently, based on these digital footprints, a comprehensive spatio-temporal analysis is performed, which captures the distribution of reading conversions and genres, as well as the regularities of reading habits. Ultimately, understanding the spatio-temporal patterns of readers can significantly enhance reading promotion practices in China.

Nowadays, the commercial potential of live e-commerce is being continuously explored, and machine vision algorithms are gradually attracting the attention of marketers and researchers. During live streaming, the visuals can be effectively captured by algorithms, thereby providing additional data support. This paper aims to consider the diversity of live streaming devices and proposes an extremely lightweight and high-precision model to meet different requirements in live streaming scenarios. Building upon yolov5s, we incorporate the MobileNetV3 module and the CA attention mechanism to optimize the model. Furthermore, we construct a multi-object dataset specific to live streaming scenarios, including anchor facial expressions and commodities. A series of experiments have demonstrated that our model realized a 0.4% improvement in accuracy compared to the original model, while reducing its weight to 10.52%.