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Mathematics
Special Issues
Social Phenomena: Mathematical Modeling and Data Analysis
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Social Phenomena: Mathematical Modeling and Data Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 491

Special Issue Editor

Dr. Yerali Gandica

E-Mail Website
Guest Editor
1. Escuela Politécnica Superior, Universidad Antonio de Nebrija, Madrid, Spain
2. Department of Mathematics, Valencian International University—VIU, Valencia, Spain
Interests: agent-based models; network science; complex systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social phenomena emerge as a consequence of the interaction between social inhabitants. For a long time, scholars from different backgrounds, especially from social sciences, have been studying several dimensions of social phenomena. However, some fundamental properties have still been outside the reach of researchers, mainly for the lack of large amounts of reliable data. The increasing amount of data that is being gathered in this digital age is progressively opening up the possibility to uncover some social patterns not so far detected, as well as pioneering the possibility for the testing of models of social patterns as a collective effect of interaction among single individuals.

This Special Issue welcomes contributions to the study of social phenomena in their broad spectrum, from simulations to social data analysis. Some potential topics are agent-based models, opinion dynamics, contagion models, culture models, social network analysis, computational social science, digital human analysis, geographical patterns, network science, big data, etc. 

Dr. Yerali Gandica
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • social phenomena
  • computational social science
  • network science
  • geographical patterns
  • social big data

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Published Papers (1 paper)

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Research

32 pages, 1553 KiB  
Article
A Fuzzy Logic Framework for Text-Based Incident Prioritization: Mathematical Modeling and Case Study Evaluation
by Arturo Peralta, José A. Olivas and Pedro Navarro-Illana
Mathematics 2025, 13(12), 2014; https://doi.org/10.3390/math13122014 - 18 Jun 2025
Viewed by 261
Abstract
Incident prioritization is a critical task in enterprise environments, where textual descriptions of service disruptions often contain vague or ambiguous language. Traditional machine learning models, while effective in rigid classification, struggle to interpret the linguistic uncertainty inherent in natural language reports. This paper [...] Read more.
Incident prioritization is a critical task in enterprise environments, where textual descriptions of service disruptions often contain vague or ambiguous language. Traditional machine learning models, while effective in rigid classification, struggle to interpret the linguistic uncertainty inherent in natural language reports. This paper proposes a fuzzy logic-based framework for incident categorization and prioritization, integrating natural language processing (NLP) with a formal system of fuzzy inference. The framework transforms semantic embeddings from incident reports into fuzzy sets, allowing incident severity and urgency to be represented as degrees of membership in multiple categories. A mathematical model based on Mamdani-type inference and triangular membership functions is developed to capture and process imprecise inputs. The proposed system is evaluated on a real-world dataset comprising 10,000 incident descriptions from a mid-sized technology enterprise. A comparative evaluation is conducted against two baseline models: a fine-tuned BERT classifier and a traditional support vector machine (SVM). Results show that the fuzzy logic approach achieves a 7.4% improvement in F1-score over BERT (92.1% vs. 85.7%) and a 12.5% improvement over SVM (92.1% vs. 79.6%) for medium-severity incidents, where linguistic ambiguity is most prevalent. Qualitative analysis from domain experts confirmed that the fuzzy model provided more interpretable and context-aware classifications, improving operator trust and alignment with human judgment. These findings suggest that fuzzy modeling offers a mathematically sound and operationally effective solution for managing uncertainty in text-based incident management, contributing to the broader understanding of mathematical modeling in enterprise-scale social phenomena. Full article
(This article belongs to the Special Issue Social Phenomena: Mathematical Modeling and Data Analysis)
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