Special lssues

Applying Computational Intelligence to Social Science Research

Submission Deadline: 01 September 2023 (closed)

Guest Editors

Ansel Y. Rodríguez González, Unidad de Transferencia Tecnológica Tepic, Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California, Mexico
Miguel Ángel Álvarez Carmona, Unidad Monterrey, Centro de Investigación en Matemáticas, A.C, Mexico
Ramón Aranda, Unidad Mérida, Centro de Investigación en Matemáticas, A.C, Mexico
Angel Díaz Pacheco, Universidad de Guanajuato, Mexico

Summary

The new technological paradigms and the massive quantities of data available for analysis have introduced important challenges to research at all levels. To address such obstacles, many advances in the computational sciences have been employed to automate and improve the precision and efficiency of standard research methods. This special issue is interested in providing a comprehensive overview of the state-of-the-art techniques to model, optimize, quantify, or support decisions in the following fields Computational Intelligence Areas and social science disciplines.


Keywords

Computational Intelligence, Artificial Neural Networks, Data Mining, Data Science, Deep Learning, Evolutionary Computation, Fuzzy reasoning, Machine Learning, Natural Language Processing techniques, Probabilistic Methods, Time Series Forecasting, Applied, Social Science, Anthropology, Communication studies, Economy, Education, Geography, Management, Political sciences, Psychology, Sociology, Tourism

Published Papers


  • Open Access

    ARTICLE

    ABMRF: An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu

    Aiman, Muhammad Arshad, Bilal Khan, Khalil Khan, Ali Mustafa Qamar, Rehan Ullah Khan
    Intelligent Automation & Soft Computing, DOI:10.32604/iasc.2024.045402
    (This article belongs to the Special Issue: Applying Computational Intelligence to Social Science Research)
    Abstract This study explores the area of Author Profiling (AP) and its importance in several industries, including forensics, security, marketing, and education. A key component of AP is the extraction of useful information from text, with an emphasis on the writers’ ages and genders. To improve the accuracy of AP tasks, the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1 (ABM1) and Random Forest (RF). The work uses an extensive technique that involves text message dataset pretreatment, model training, and assessment. To evaluate the effectiveness of several machine learning (ML) algorithms in classifying age and gender, including Composite Hypercube… More >

  • Open Access

    ARTICLE

    Deep Learning Model for News Quality Evaluation Based on Explicit and Implicit Information

    Guohui Song, Yongbin Wang, Jianfei Li, Hongbin Hu
    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 275-295, 2023, DOI:10.32604/iasc.2023.041873
    (This article belongs to the Special Issue: Applying Computational Intelligence to Social Science Research)
    Abstract Recommending high-quality news to users is vital in improving user stickiness and news platforms’ reputation. However, existing news quality evaluation methods, such as clickbait detection and popularity prediction, are challenging to reflect news quality comprehensively and concisely. This paper defines news quality as the ability of news articles to elicit clicks and comments from users, which represents whether the news article can attract widespread attention and discussion. Based on the above definition, this paper first presents a straightforward method to measure news quality based on the comments and clicks of news and defines four news quality indicators. Then, the dataset… More >

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