User Profiling from Textual Data: A Systematic Literature Review

• Jemal A. Jilo UTFPR
• Felipe Peixoto UTFPR
• Myriam Delgado UTFPR
• Thiago H. Silva UTFPR / University of Toronto

DOI: https://doi.org/10.5753/webmedia.2026.30315

Resumo

This review examines current research on user profiling from textual data. The adopted methodology encompasses a systematic literature search, the selection of peer-reviewed studies based on predefined eligibility criteria, and a quality assessment of their research design, datasets, and machine learning methods. We conducted the search using multiple querying strategies across ACM DL, IEEE Xplore, Scopus, Web of Science, ScienceDirect, Springer-Link, and Google Scholar as a complementary source. The search retrieved 133 studies using different query formulations. After applying the predefined evaluation criteria, we selected 37 studies for detailed analysis, of which 3 are surveys. These studies investigate three main categories of user profiling dimensions: (1) demographic and sociodemographic factors, (2) personality and psychographic traits, and (3) behavioral patterns, preferences, and intent. The selected studies employ a wide range of methods, including classical machine learning, deep learning, BERT-based models, and large language models (LLMs). Regarding datasets, Twitter is the most frequently used platform and supports studies on gender, occupation, socioeconomic status, personality, and political profiling. Researchers also use data from Reddit, Amazon, and platform-specific corpora covering Russian, Arabic, Turkish, Persian, Korean, and Punjabi. However, English remains the predominant language in the datasets used by most studies. Evaluation practices are highly heterogeneous, with studies employing metrics such as accuracy, F1-score, macro-F1, AUC, correlation, error-based measures, and clustering quality indices. Despite recent advances, several challenges remain, including limited dataset diversity, demographic bias, feature sparsity, and computational constraints. In light of these challenges, and based on the review findings, we emphasize the importance of adopting multilingual and cross-cultural datasets, explainable AI frameworks, and standardized evaluation protocols to improve replicability and support the ethical deployment of user profiling systems. Overall, this review consolidates the current state of the art and identifies promising directions for developing more robust, transparent, and responsible approaches to user profiling from textual content.

Palavras-chave: User Profiling, User Attribute Inference, Textual Data, Natural Language Processing, Web-based Systems, Systematic Literature Review

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