Hybrid artificial intelligence algorithm for target audience analysis in social networks
Keywords:
Audience segmentation, user behavior modeling, community detection, graph analytics, social media mining, TF-IDF weighting, RF classifier, K-means clustering, predictive analytics, multi-platform data integrationSynopsis
The rapid advancement of technology has significantly increased the number of social network users and resulted in the generation of vast amounts of data. Consequently, extensive opportunities have emerged for understanding user interests, behaviors, and interactions. In particular, the detection and prediction of target groups are of critical importance for achieving effective analytical and decision-making outcomes. Classical social network analysis methods are typically designed for the analysis of small-scale and highly interconnected networks. Furthermore, focusing solely on textual content is insufficient to capture collective behavior, as network topology is often overlooked in many studies. Similarly, analyzing data from a single social media platform does not provide a comprehensive understanding of user behavior patterns. This study proposes a hybrid artificial intelligence model for target audience analysis in social networks by integrating text mining, behavioral analytics, clustering techniques, classification algorithms, and graph-based social network analysis. To obtain the data used in the model, datasets from multiple social media platforms were employed, including Twitter, Reddit, Instagram, Facebook, TikTok, and YouTube. The primary reason for selecting these platforms is the public availability of their datasets and their accessibility through the Kaggle platform. This enables the automated acquisition and processing of structured user activity data within the proposed system. The proposed model is based on a hybrid architecture that combines multiple artificial intelligence techniques. Analytical methods, clustering algorithms, and classification models are integrated within the framework. In addition, a graph-based approach is employed, where relationships are modeled through node and edge structures to represent social interactions more effectively. During the research process, the dataset was divided into two subsets: 80% of the data was used for model training, while the remaining 20% was reserved for testing and evaluation purposes. To assess the performance of the proposed model, standard evaluation metrics including accuracy, precision, recall, and F1-score were utilized. The principal advantage of the proposed framework lies in its ability to integrate text analysis, behavioral analysis, and graph-based social relationship analysis within a unified artificial intelligence architecture, thereby providing a more comprehensive and effective approach to target group detection and prediction in social networks.
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Published
August 26, 2025
Copyright (c) 2026 Masuma Mammadova, Alibeyli Turkan, Gasimov Huseyn
How to Cite
Mammadova, M., Alibeyli, T., & Gasimov, H. (2025). Hybrid artificial intelligence algorithm for target audience analysis in social networks. In S. Volosovych, L. Morozova, N. Husarevych, M. Mammadova, N. Bayramov, Z. Jabrayilova, T. Alibeyli, & H. Gasimov, In Press. Digital intelligence: AI in industry and business. Scientific Route OÜ®. https://monograph.route.ee/rout/catalog/book/digital_intelligence_ai_in_industry_and_business/chapter/210
