Open Access
HARNESSING AI FOR PROACTIVE PUBLIC RELATIONS: A FRAMEWORK FOR PREDICTING AND CAPITALIZING ON SOCIAL MEDIA TRENDS
4
Department of Digital Media & Public Relations, Indian Institute of Mass Communication (IIMC), New Delhi, India
4
Department of Media and Communication Studies, University of Mumbai, Mumbai, India
Abstract
Purpose: The proliferation of social media has fundamentally altered the public relations (PR) landscape, demanding a shift from reactive damage control to proactive, data-driven strategy. This paper addresses the growing need for a systematic approach to leveraging Artificial Intelligence (AI) for predictive social media analysis within the PR domain. It aims to bridge the gap between theoretical AI capabilities and practical PR application by proposing a comprehensive framework for identifying, analyzing, and acting upon emerging digital trends.
Methods: A systematic literature review was conducted to synthesize current research on AI applications in social media analytics—including sentiment analysis, topic modeling, and popularity prediction—and the integration of AI into PR workflows. Drawing from 12 seminal academic and industry sources, this review forms the foundation for the development of a novel conceptual framework.
Results: The research culminates in the proposal of the Predictive AI Framework for PR (PAFP). This four-phase framework outlines a structured process for (1) Data Aggregation & Filtering, (2) Trend Identification & Analysis, (3) Trajectory & Impact Prediction, and (4) Strategic PR Application. The framework integrates advanced AI techniques, such as Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNNs), into a cohesive workflow designed to provide actionable, forward-looking intelligence for communication professionals. A hypothetical case study is presented to illustrate its practical utility.
Conclusion: The PAFP provides a vital strategic tool, enabling PR professionals to move beyond mere monitoring to predictive intelligence. By harnessing AI, practitioners can anticipate public discourse, mitigate crises before they escalate, and craft more resonant and timely campaigns. This paper argues that the adoption of such frameworks is not merely an opportunity but a necessity for the future relevance and efficacy of the public relations profession.
Keywords
Artificial Intelligence (AI),
Public Relations (PR),
Social Media Analytics,
Trend Prediction
References
Meltwater; We Are Social. Digital 2024: Global Overview Report. [Electronic resource] Access mode: https://datareportal.com/reports/digital-2024-global-overview-report (date accessed: 17.06.2025).
Alshattnawi S. et al. Beyond word-based model embeddings: Contextualized representations for enhanced social media spam detection //Applied Sciences. – 2024. – Vol. 14 (6). – pp. 1-25. https://doi.org/10.3390/app14062254.
Pandey R. et al. Hybrid attention-based long short-term memory network for sarcasm identification //Applied Soft Computing. – 2021. – Vol. 106. https://doi.org/10.1016/j.asoc.2021.107348.
Musliadi K. H., Zainuddin H., Wabula Y. Twitter social media conversion topic trending analysis using latent Dirichlet allocation algorithm //Journal of Applied Engineering and Technological Science (JAETS). – 2022. – Vol. 4 (1). – pp. 390-399.
Samantapudi, R. K. R. (2025). Advantages & impact of fine tuning large language models for ecommerce search. Journal of Information Systems Engineering and Management, 10(45s), 600–622. https://doi.org/10.52783/jisem.v10i45s.8898
Chan J. Y. L. et al. State of the art: a review of sentiment analysis based on sequential transfer learning //Artificial Intelligence Review. – 2023. – Vol. 56 (1). – pp. 749-780.
Jin R., Liu X., Murata T. Predicting popularity trend in social media networks with multi-layer temporal graph neural networks //Complex & Intelligent Systems. – 2024. – Vol. 10 (4). – pp. 4713-4729.
Wang J. et al. Social media popularity prediction with multimodal hierarchical fusion model //Computer Speech & Language. – 2023. – Vol. 80. https://doi.org/10.1016/j.csl.2023.101490.
Srilatha, S. (2025). Integrating AI into enterprise content management systems: A roadmap for intelligent automation. Journal of Information Systems Engineering and Management, 10(45s), 672–688. https://doi.org/10.52783/jisem.v10i45s.8904
Giovanola B., Granata P. Ethics for human-centered education in the age of AI //Entrepreneurship and Digital Humanities. – Edward Elgar Publishing, 2024. – pp. 96-109.
Yue C. A. et al. Public relations meets artificial intelligence: Assessing utilization and outcomes //Journal of Public Relations Research. – 2024. – Vol. 36 (6). – pp. 513-534.
Sardana, J., & Mukesh Reddy Dhanagari. (2025). Bridging IoT and Healthcare: Secure, Real-Time Data Exchange with Aerospike and Salesforce Marketing Cloud. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3853
Gartner Predicts 75% of Analytics Content to Use GenAI for Enhanced Contextual Intelligence by 2027. [Electronic resource] Access mode: https://www.gartner.com/en/newsroom/press-releases/2025-06-18-gartner-predicts-75-percent-of-analytics-content-to-use-genai-for-enhanced-contextual-intelligence-by-2027 (accessed: 06/25/2025).
Talkwalker. Social Media Trends Report 2024. [Electronic resource] Access mode: https://www.talkwalker.com/social-media-trends (date of access: 06/27/2025).
AI for Sales and Marketing Market worth $240.58 billion by 2030. [Electronic resource] Access mode: https://www.marketsandmarkets.com/PressReleases/ai-for-sales-and-marketing.asp (date accessed: 01.07.2025).
Rangu, S. (2025). Analyzing the impact of AI-powered call center automation on operational efficiency in healthcare. Journal of Information Systems Engineering and Management, 10(45s), 666–689. https://doi.org/10.55278/jisem.2025.10.45s.666
Most read articles by the same author(s)
- Ali Hassan Kareem, Mariam Saad, Machine Learning-Based SAP Production Planning Framework for Demand Prediction, Inventory Reduction, and Throughput Optimization , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 10 (2026): Volume 03 Issue 10
- Alexander V. Korovin, Optimizing Zero-Downtime Microservice Deployments: Integrating DevOps Principles in .NET Core Environments , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 01 (2026): Volume 03 Issue 01
- Yuki Nakamura, Hiroshi Tanaka, A SEMANTIC METRIC LEARNING APPROACH FOR ENHANCED MALWARE SIMILARITY SEARCH , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 01 (2025): Volume 02 Issue 01
- Yuki Nakamura, Isabella Romano, HYBRID DEEP LEARNING FOR TEXT CLASSIFICATION: INTEGRATING BIDIRECTIONAL GATED RECURRENT UNITS WITH CONVOLUTIONAL NEURAL NETWORKS , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 04 (2025): Volume 02 Issue 04
- Dr. Tanay Deshpande, Dr. Kavita Sharma, ADVANCING ARTIFICIAL INTELLIGENCE: AN IN-DEPTH LOOK AT MACHINE LEARNING AND DEEP LEARNING ARCHITECTURES, METHODOLOGIES, APPLICATIONS, AND FUTURE TRENDS , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 01 (2025): Volume 02 Issue 01
- Vaibhav Tummalapalli, Cohort-Based Segmentation Framework for Machine Learning: Structuring Temporal Data for Enhanced Feature Engineering , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 03 (2026): Volume 03 Issue 03
- Vaibhav Tummalapalli, A Framework for Adjusting Oversampling Bias in Machine Learning Models , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Abdullah Khalid Al-Harbi, Noor Fahad Al-Qahtani, A Quantum-Inspired Computational Architecture for Efficient High-Dimensional Data Processing , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 10 (2026): Volume 03 Issue 10
Similar Articles
- Dr. Oliver Henry Mitchell, A Comprehensive Framework for Intelligent Data Analytics in Modern Intelligent Systems: Design, Methods, and Applications , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 05 (2026): Volume 03 Issue 05
- Dr. Javier M. Ortega, Dr. Lucia Fernández-Ríos, Predictive Modeling of Online Retail Revenue Using Data Exploration and Intelligent Algorithms , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Emil Novak, Deep Learning For E‑Commerce Recommendations: Capturing Long- And Short-Term User Preferences With Cnn-Based Representation Learning , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Dr. Minh Quang Tran, Dr. Lan Anh Nguyen, An Advanced Analytical Architecture for Leveraging Big Data in Artificial Intelligence Systems: Techniques, Optimization Strategies, and Case-Based Evaluation , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Lucas Vermeulen, Sophie De Smet, Dr. Thomas Dubois, Integrated Temporal Analytics and AI-Based Approaches for Predicting Culinary Ingredient Consumption Patterns: Evidence from Thai Markets , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Dr. Kwame Mensah, Dr. Abena Owusu, An Interpretable Visual Analytics Framework for Machine Learning–Based Multichannel Time Series Classification and Performance Evaluation , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Dr. Isabella Müller, Samuel Moyo, UNLOCKING SYNERGIES: A FRAMEWORK FOR INTEGRATING ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGIES , International Journal of Intelligent Data and Machine Learning: Vol. 2 No. 07 (2025): Volume 02 Issue 07
- Dr. Hana Bekele Tadesse, Intelligent Sequential Analytics Framework for Enhancing Monetary Transfer Scheduling in Logistics-Based Financial Systems , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 02 (2026): Volume 03 Issue 02
- Muhammad Rizky Pratama, Siti Aulia Rahma, Interpretable Predictive Analytics Approach for Robust Financial Risk Assessment and Forecasting , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Dr. Rohan Pillay, Dr. Ananya Naidoo, Comparative Analysis of Machine Learning Approaches for Cardiovascular Risk Assessment , International Journal of Intelligent Data and Machine Learning: Vol. 3 No. 08 (2026): Volume 03 Issue 08
1-10 of 56
Next
You may also start an advanced similarity search for this article.