Commenting on recommended short videos: the perspective of social learning

Purpose Short videos have increasingly become important social media applications. Comments which are visible as a thread under short videos play a critical role in influencing public opinion on the short videos. Commenting behavior on recommended short videos reflects transfer from human intelligence interaction to the most active human–human interaction. The purpose of this study is to explore the influencing mechanism of commenting behavior on recommended short videos. Design/methodology/approach Social learning theory suggests two patterns of social learning, namely learning through modeling and learning by direct experience. Drawing on social learning theory, this study employs the grounded theory to explore influencing mechanism of commenting behavior. During open coding, 170 initial concepts and 30 subcategories were elicited. During axial coding, nine categories were elicited. During selective coding, a theoretical model was developed based on the identification of relations among categories. Findings Learning through modeling is embodied in observational learning, herd commenting and learning through norms, all of which have direct impacts on commenting behavior. Meanwhile, learning by direct experience is embodied in positive reinforcement learning and negative reinforcement learning, both of which have direct impacts on commenting behavior. Moreover, information quality as well as cognition and emotion have direct impacts on commenting behavior. Practical implications Managers and developers of short videos should design more functions so that users could select whose comments are visible and what comments are visible in terms of subjects and emotions. More functions can be designed so that users engage in learning from commenting experience in terms of positive results. Originality/value This study investigates commenting behavior on recommended short videos from the perspective of social learning, presenting a new lens for research.

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Publication Details

Journal
Aslib Journal of Information Management
Published
2025-08-18
DOI
https://doi.org/10.1108/ajim-10-2024-0793
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
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article

Commenting on recommended short videos: the perspective of social learning

Yalan Yan, Xianjin Zha, Yan Gao, Xue Wang et al.
Aslib Journal of Information Management
Technology Adoption and User Behaviour
article

Commenting on recommended short videos: the perspective of social learning

Yalan Yan, Xianjin Zha, Yan Gao, Xue Wang, Qinquan Dai
article en

Abstract

Purpose Short videos have increasingly become important social media applications. Comments which are visible as a thread under short videos play a critical role in influencing public opinion on the short videos. Commenting behavior on recommended short videos reflects transfer from human intelligence interaction to the most active human–human interaction. The purpose of this study is to explore the influencing mechanism of commenting behavior on recommended short videos. Design/methodology/approach Social learning theory suggests two patterns of social learning, namely learning through modeling and learning by direct experience. Drawing on social learning theory, this study employs the grounded theory to explore influencing mechanism of commenting behavior. During open coding, 170 initial concepts and 30 subcategories were elicited. During axial coding, nine categories were elicited. During selective coding, a theoretical model was developed based on the identification of relations among categories. Findings Learning through modeling is embodied in observational learning, herd commenting and learning through norms, all of which have direct impacts on commenting behavior. Meanwhile, learning by direct experience is embodied in positive reinforcement learning and negative reinforcement learning, both of which have direct impacts on commenting behavior. Moreover, information quality as well as cognition and emotion have direct impacts on commenting behavior. Practical implications Managers and developers of short videos should design more functions so that users could select whose comments are visible and what comments are visible in terms of subjects and emotions. More functions can be designed so that users engage in learning from commenting experience in terms of positive results. Originality/value This study investigates commenting behavior on recommended short videos from the perspective of social learning, presenting a new lens for research.

Aslib Journal of Information Management
Wuhan University of Technology (CN), Wuhan University (CN), Hubei University of Automotive Technology (CN), Wuhan University of Science and Technology (CN)
Openalex Percentile: Top 77%
Technology Adoption and User Behaviour
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