ASMMC21: The 6th International Workshop on Affective Social Multimedia Computing

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Abstract

Affective social multimedia computing is an emergent research topic for both affective computing and multimedia research communities. Social multimedia is fundamentally changing how we communicate, interact, and collaborate with other people in our daily lives. Comparing with well-organized broadcast news and professionally made videos such as commercials, TV shows, and movies, social multimedia media computing imposes great challenges to research communities. Social multimedia contains much affective information. Effective extraction of affective information from social multimedia can greatly help social multimedia computing (e.g., processing, index, retrieval, and understanding). Although much progress have been made in traditional multimedia research on multimedia content analysis, indexing, and retrieval based on subjective concepts such as emotion, aesthetics, and preference, affective social multimedia computing is a new research area. The affective social multimedia computing aims to proceed affective information from social multi-media. For massive and heterogeneous social media data, the research requires multidisciplinary understanding of content and perceptual cues from social multimedia. This workshop served as a successful step towards this goal and attracted contributions from different research disciplines on the analysis of affective signals in interaction (multimodal analyses enabling artificial agents in Human-Machine Interaction, social Interaction with artificial agents) and social multimedia (e.g., twitter, wechat, weibo, youtube, facebook, etc). This paper provides a summary of the activities of the workshop and the accepted papers and abstracts.

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Huang, D., Schuller, B., Tao, J., Xie, L., & Yang, J. (2021). ASMMC21: The 6th International Workshop on Affective Social Multimedia Computing. In ICMI 2021 - Proceedings of the 2021 International Conference on Multimodal Interaction (pp. 864–867). Association for Computing Machinery, Inc. https://doi.org/10.1145/3462244.3480980

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