Machine Learning Techniques for Social Media Data Analysis
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 386
Special Issue Editor
Interests: social media analysis; natural language processing; machine learning; deep learning; agent-based modeling; semantic web
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The widespread adoption of social media platforms has led to the continuous generation of large-scale, dynamic, and heterogeneous data that reflect human behavior, opinions, interactions, and information diffusion in real time. This data offers significant opportunities for analyzing and understanding public opinion on various topics, but it also poses significant challenges due to its volume, velocity, variety, and inherent noise. Addressing these challenges requires robust, scalable, and interpretable machine learning techniques capable of transforming raw social media data into actionable knowledge.
This Special Issue focuses on the development, adaptation, and evaluation of machine learning techniques—ranging from traditional supervised and unsupervised learning methods to deep learning and hybrid approaches—designed to extract meaningful insights from social media content. Contributions may address textual, visual, audio, temporal, network-based, or multimodal data and explore tasks such as sentiment and emotion analysis, opinion mining, stance detection, user behavior modeling, engagement prediction, misinformation and fake news detection, recommendation systems, and social network analysis. Both methodological innovations and application-driven studies are welcome, particularly those demonstrating robustness, scalability, explainability, and ethical awareness.
The Special Issue aims to consolidate recent research on machine learning techniques for social media data analysis by presenting state-of-the-art methods, applications, and emerging research directions. Additionally, it seeks to promote methodological innovation by encouraging contributions that address key challenges such as data heterogeneity, scalability, explainability, robustness, and ethical considerations in the analysis of social media data. Third, it intends to foster interdisciplinary perspectives by bringing together research from computer science, data science, economics, and the social sciences, highlighting how machine learning-based social media analysis can support both theoretical understanding and practical decision-making across diverse application domains.
Prof. Dr. Liviu-Adrian Cotfas
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 250 words) can be sent to the Editorial Office for assessment.
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. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- social media data analysis
- machine learning
- deep learning
- natural language processing
- sentiment analysis
- opinion mining
- user behavior modeling
- social network analysis
- misinformation and fake news detection
- multimodal data analysis
- predictive analytics
- explainable artificial intelligence (XAI)
- ethical and responsible AI
- big data analytics
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