A Review on Emotion Detection in Online Social Networks: Multi-Label Learning Approach
DOI:
https://doi.org/10.15662/IJARCST.2025.0805013Keywords:
Emotion Recognition, Multi-Label Classification, Social Network Analysis, BERT, Reinforcement Learning, Context-Aware AI, Micro-Emotion Indicators, Awareness Features, Real-Time Emotion Detection, Human-AI Interaction, Gamified Learning, Adaptive Emotion Modeling, Deep Learning, Multi-Modal Emotion AnalysisAbstract
Emotion recognition in online social networks has grown sophisticated, particularly with the advent of multi-label emotions represented across varied content such as text, images, and videos . Such expressions usually manage to indicate more than one emotion at a time, with users in secluded online environments having greater likelihoods of misinterpretation. While AI models like BERT with multi-label classifiers have shown a maximum of 97.5% accuracy in emotion detection, they do so without label correlations and without live multi-modal interventions. Evidence also indicates that small interactive learning and context-aware features built into models are superior to static classification or single-labeling techniques in expressing emotional subtlety. In this work, we introduce a multi-stage multi-label learning paradigm to identify emotions within social networks by integrating AI-based identification with correlation-backed methods.
Our model operates in four phases: Awareness Features, Micro-Emotion Indicators, Reinforcement Learning, and AI-Driven Multi-Label Classification. Collectively, these phases enable models to demonstrate multiple emotions, delay before labeling, and construct correct emotional profiles as time passes. This dual-modeling strategy seeks to enhance emotion detection as an engaging, personalized, and effective practice in real social network environments.
References
1) Weinz, M., Zannone, N., Allodi, L., & Apruzzese, G. (May 2025). The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-generated Phishing Emails against Organizations.
https://arxiv.org/abs/2505.12104.
2) Lain, D., Jost, T., Matetic, S., Kostiainen, K., & Capkun, S. (Dec 2024). Content, Nudges and Incentives: A Study on the Effectiveness and Perception of Embedded Phishing Training.
https://arxiv.org/abs/2409.01378
3) Heiding, F., Lermen, S., Kao, A., Schneier, B., & Vishwanath, A. (Nov 2024). Evaluating Large Language Models' Capability to Launch Fully Automated Spear Phishing Campaigns: Validated on Human Subjects.
https://arxiv.org/abs/2412.00586
4) Chen, X., Sacré, M., Lenzini, G., Greiff, S., Distler, V., & Sergeeva, A. (Feb 2024). The Effects of Group Discussion and Role-playing Training on Self-efficacy, Support-seeking, and Reporting Phishing Emails.
https://arxiv.org/abs/2402.11862
5) Zheng, S.Y. et al. (2023). Checking, Nudging or Scoring? Evaluating E-mail User Security Tools.
https://www.usenix.org/system/files/soups2023-zheng.pdf
6) Distler, V. et al. (2023). The Influence of Context on Response to Spear-Phishing Attacks: an In-Situ Deception Study.
https://dl.acm.org/doi/10.1145/3544548.3581170
7) Baltuttis, D. (2024). Effects of Visual Risk Indicators on Phishing Detection Behavior: An Eye-Tracking Experiment.
https://www.sciencedirect.com/science/article/pii/S0167404824002451
8) Singkeruang, A.W.T.F., Susanto, S.E., & Saeni, N. (2025). Mitigating the Risk of Qushing Threats Using the Security Behavior Intentions Scale (SeBIS).
9) Al Subaiey, A. et al. (2024). Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection.
https://arxiv.org/abs/2405.11619
10) Saha Roy, S., Torres, C., & Nilizadeh, S. (May 2025). "Explain, Don't Just Warn!" -- A Real-Time Framework for Generating Phishing Warnings with Contextual Cues.
https://arxiv.org/abs/2505.06836
11) Stalans, L. (2023). Predicting Phishing Victimization: Comparing Prior Victimization, Cognitive, and Emotional Styles, and Vulnerable or Protective E-mail Strategies.
https://www.tandfonline.com/doi/full/10.1080/15564886.2023.2218369
12) Williamson, S.M. (2024). The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation.
https://www.mdpi.com/2078-2489/15/6/299
13) Agha, Z. (2024). A systematic review on design-based nudges for adolescent online safety.
https://www.sciencedirect.com/science/article/pii/S2212868924000710
14) Sumner, J. (2023). Developing an Artificial Intelligence-Driven Nudge Intervention to Improve Medication Adherence: A Human-Centred Design Approach.
https://pmc.ncbi.nlm.nih.gov/articles/PMC10709244/
15) Huang, L., Jia, S., Balcetis, E., & Zhu, Q. (2021). ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention.
https://arxiv.org/abs/2106.06907
16) Zhuo, S., Biddle, R., Koh, Y.S., Lottridge, D., & Russello, G. (2022). SoK: Human-Centered Phishing Susceptibility.
https://arxiv.org/abs/2202.07905
17) A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions.
18) Major Energy Company Targeted in Large QR Code Phishing Campaign https://cofense.com/blog/major-energy-company-targeted-in-large-qr-code-campaign/
