Machine Learning in Social Media Analysis

Dear Colleagues,

The pervasive influence of social media platforms has transformed communication, social interactions, and culture, generating an unprecedented volume of user-generated content. This rich, complex, and often unstructured data presents both significant opportunities and formidable challenges for analysis. Machine learning (ML) techniques are proving indispensable in unlocking the insights hidden within this vast digital landscape, enabling the detection of trends, sentiment, misinformation, and community structures. This Special Issue aims to explore the cutting-edge advancements and applications of machine learning in the realm of social media analysis.

We invite original research and comprehensive review articles that advance theoretical understanding, propose novel methodologies, or present innovative practical applications of ML across diverse social media contexts and platforms. We especially encourage interdisciplinary contributions bridging computer science with fields such as communication, sociology, political science, and media studies.

We welcome submissions on, but not limited to, the following topics:

• Social Network Analysis Using Machine Learning;
• Sentiment Analysis and Emotion Detection in Social Media Data;
• Misinformation and Fake News Detection;
• Prediction of Social Phenomena (e.g., user behaviors, engagement patterns, content virality, and positive/negative reactions);
• Community Detection and Analysis;
• Natural Language Processing (NLP) for Social Media Content Understanding;
• Recommender Systems for Social Media Content;
• Ethical Considerations and Bias in Social Media ML Models;
• Privacy-Preserving Machine Learning for Social Data;
• Cross-Platform Social Media Analysis;
• Evolutionary Dynamics and Trend Prediction on Social Media;
• Machine Learning for Political Discourse Analysis on Social Media, Including Public Opinion; Modeling and Election Forecasting;
• Generative AI Applications in Social Media, Including Content Creation, Analysis, and Detection of Synthetic Media (e.g., deepfakes);
• Impact of Generative AI on Information Dissemination and Public Discourse;
• Multimodal Machine Learning for Social Media (text, image, audio, and video integration).

Dr. Kellyton Brito
Dr. Vinícius Vieira
Dr. Pablo Sampaio
Guest Editors

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. Informatics is an international peer-reviewed open access quarterly 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 1800 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.

• machine learning
• social media
• natural language processing (NLP)
• sentiment analysis
• network analysis
• user behavior prediction
• data mining
• misinformation detection
• computational social science
• online communities
• generative AI