Algorithmic Agitation and Affective Engineering: AI-Driven Emotion Recognition and Strategic Communication in Contemporary Social Movements

Research Article
Open access

Algorithmic Agitation and Affective Engineering: AI-Driven Emotion Recognition and Strategic Communication in Contemporary Social Movements

Xingchen Zhou 1*
  • 1 The University of Queensland, Brisbane, Queensland 4072, Australia    
  • *corresponding author rara481846778@gmail.com
ACE Vol.163
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-159-4
ISBN (Online): 978-1-80590-160-0

Abstract

In the current context where the digital social movement increasingly influences the shape of political discourse, artificial intelligence demonstrates a dual effect by capturing and guiding collective emotions. This study explores the intersection of affective computing and strategic communication, revealing how AI reconstructs the emotional mobilization mechanism and ethical framework in protest activities. Based on an interdisciplinary theoretical perspective, we apply facial recognition models, transformative text classifiers, and multimodal fusion technologies to conduct sentiment mapping of protest content on social platforms. Experimental data show that the emotion recognition scheme integrating multiple modalities has the highest classification accuracy (F1= 0.89), which makes it possible to accurately anchor emotions. The communication strategy designed from emotional portraits such as anger and fear increased the interactive participation rate by up to 42%. However, technological empowerment comes with profound ethical risks—emotional manipulation can undermine the authenticity of public discussions, and data collection also faces litigation for privacy violations. The research empirically reveals the reshaping effect of emotional intelligence tools on the shape of collective actions and proposes a framework for ethical application that considers both emotional resonance and social responsibility, delineating the limits of technological intervention in the public domain.

Keywords:

AI Emotion Recognition, Affective Computing, Strategic Communication, Social Movements, Multimodal Models

Zhou,X. (2025). Algorithmic Agitation and Affective Engineering: AI-Driven Emotion Recognition and Strategic Communication in Contemporary Social Movements. Applied and Computational Engineering,163,15-20.
Export citation

References

[1]. Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. arXiv preprint arXiv:2203.06935.

[2]. Hu, D., Hou, X., Wei, L., Jiang, L., & Mo, Y. (2022). MM-DFN: Multimodal dynamic fusion network for emotion recognition in conversations. arXiv preprint arXiv:2203.02385.

[3]. Hu, J., Liu, Y., Zhao, J., & Jin, Q. (2021). MMGCN: Multimodal fusion via deep graph convolution network for emotion recognition in conversation. arXiv preprint arXiv:2107.06779.

[4]. Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. arXiv preprint arXiv:2203.06935.

[5]. Ruckenstein, M. (2023). The feel of algorithms. University of California Press.

[6]. WEMAC: Women and Emotion Multi-modal Affective Computing Dataset. (2024). Nature Scientific Data, 11, Article 40.

[7]. Affective Computing: Recent Advances, Challenges, and Future Directions. (2023). Intelligent Computing, 1(1), 76.

[8]. Emotion recognition from unimodal to multimodal analysis: A review. (2023). Information Processing & Management, 60(2), 103163.

[9]. A systematic review of trimodal affective computing approaches. (2024). Expert Systems with Applications, 213, 119719.

[10]. Multimodal emotion recognition: A comprehensive review, trends, and challenges. (2023). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), e1563.

Cite this article

Zhou,X. (2025). Algorithmic Agitation and Affective Engineering: AI-Driven Emotion Recognition and Strategic Communication in Contemporary Social Movements. Applied and Computational Engineering,163,15-20.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning

ISBN：978-1-80590-159-4(Print) / 978-1-80590-160-0(Online)
Editor：Marwan Omar
Conference website: https://2025.confseml.org/
Conference date: 2 July 2025
Series: Applied and Computational Engineering
Volume number: Vol.163
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. arXiv preprint arXiv:2203.06935.

[2]. Hu, D., Hou, X., Wei, L., Jiang, L., & Mo, Y. (2022). MM-DFN: Multimodal dynamic fusion network for emotion recognition in conversations. arXiv preprint arXiv:2203.02385.

[3]. Hu, J., Liu, Y., Zhao, J., & Jin, Q. (2021). MMGCN: Multimodal fusion via deep graph convolution network for emotion recognition in conversation. arXiv preprint arXiv:2107.06779.

[4]. Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. arXiv preprint arXiv:2203.06935.

[5]. Ruckenstein, M. (2023). The feel of algorithms. University of California Press.

[6]. WEMAC: Women and Emotion Multi-modal Affective Computing Dataset. (2024). Nature Scientific Data, 11, Article 40.

[7]. Affective Computing: Recent Advances, Challenges, and Future Directions. (2023). Intelligent Computing, 1(1), 76.

[8]. Emotion recognition from unimodal to multimodal analysis: A review. (2023). Information Processing & Management, 60(2), 103163.

[9]. A systematic review of trimodal affective computing approaches. (2024). Expert Systems with Applications, 213, 119719.

[10]. Multimodal emotion recognition: A comprehensive review, trends, and challenges. (2023). Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), e1563.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.