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Algorithms
Special Issues
Machine Learning in Social Network Analytics
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Machine Learning in Social Network Analytics

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 7011

Special Issue Editors

Dr. Mukesh Prasad

E-Mail Website
Guest Editor
School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia
Interests: machine learning; computational intelligence; image processing; data analytics; big data; natural language processing; brain–computer interface
Special Issues, Collections and Topics in MDPI journals
Dr. Faezeh Karimi

E-Mail Website
Guest Editor
School of Computer Science, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: data analytics; social network analysis; and information system theories in education; healthcare and sustainability fields
Prof. Dr. Dinesh Vishwakarma

E-Mail Website
Guest Editor
Department of Information Technology, Delhi Technological University, Delhi 110042, India
Interests: computer vision; machine learning; deep learning; human pose estimation; crowd analysis; fake news detection; sentiment analysis; person re-identification
Dr. Zahid Akhtar

E-Mail Website
Guest Editor
Department of Network and Computer Security, State University of New York Polytechnic Institute, Utica, NY 13502, USA
Interests: machine learning and computer vision with applications to cybersecurity, biometrics, affect recognition, image and video processing, and perceptual-based audiovisual multimedia quality assessmentperceptual-based audiovisual multimedia quality assessment; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social network platforms have become an integral part of our daily lives, providing a means for people to connect, share information, and express their opinions. As a result, the data generated by these platforms have become a valuable resource for businesses, organizations, and researchers to gain insights into consumer behavior, market trends, and public opinion. However, the sheer volume and complexity of these data make it challenging to extract meaningful insights. This Special Issue aims to explore the latest techniques and applications in social media analytics, highlighting existing challenges and opportunities in this field.

This Special Issue aims to cover a wide range of topics related to social network analytics, including, but not limited to:

  • Deep learning and neural networks for social network analytics;
  • Graph-based techniques for social network analysis;
  • Natural language processing for social media text data;
  • Social network analytics for e-commerce and online platforms;
  • Social network analytics for crisis management and emergency responses;
  • Multimodal data fusion and integration for social network analytics;
  • Explainable AI and interpretability in social network analytics;
  • Ethics and privacy issues in social network analytics;
  • Real-time and streaming social network analytics;
  • Explainable AI and interpretability in social network

Dr. Mukesh Prasad
Dr. Faezeh Karimi
Prof. Dr. Dinesh Vishwakarma
Dr. Zahid Akhtar
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 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. Algorithms is an international peer-reviewed open access monthly 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 1600 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.

Published Papers (2 papers)

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25 pages, 1338 KiB  
Article
A System to Support Readers in Automatically Acquiring Complete Summarized Information on an Event from Different Sources
by Pietro Dell’Oglio, Alessandro Bondielli and Francesco Marcelloni
Algorithms 2023, 16(11), 513; https://doi.org/10.3390/a16110513 - 08 Nov 2023
Viewed by 1339
Abstract
Today, most newspapers utilize social media to disseminate news. On the one hand, this results in an overload of related articles for social media users. On the other hand, since social media tends to form echo chambers around their users, different opinions and [...] Read more.
Today, most newspapers utilize social media to disseminate news. On the one hand, this results in an overload of related articles for social media users. On the other hand, since social media tends to form echo chambers around their users, different opinions and information may be hidden. Enabling users to access different information (possibly outside of their echo chambers, without the burden of reading entire articles, often containing redundant information) may be a step forward in allowing them to form their own opinions. To address this challenge, we propose a system that integrates Transformer neural models and text summarization models along with decision rules. Given a reference article already read by the user, our system first collects articles related to the same topic from a configurable number of different sources. Then, it identifies and summarizes the information that differs from the reference article and outputs the summary to the user. The core of the system is the sentence classification algorithm, which classifies sentences in the collected articles into three classes based on similarity with the reference article: sentences classified as dissimilar are summarized by using a pre-trained abstractive summarization model. We evaluated the proposed system in two steps. First, we assessed its effectiveness in identifying content differences between the reference article and the related articles by using human judgments obtained through crowdsourcing as ground truth. We obtained an average F1 score of 0.772 against average F1 scores of 0.797 and 0.676 achieved by two state-of-the-art approaches based, respectively, on model tuning and prompt tuning, which require an appropriate tuning phase and, therefore, greater computational effort. Second, we asked a sample of people to evaluate how well the summary generated by the system represents the information that is not present in the article read by the user. The results are extremely encouraging. Finally, we present a use case. Full article
(This article belongs to the Special Issue Machine Learning in Social Network Analytics)
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Review

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17 pages, 2718 KiB  
Review
Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks
by Hamed Taherdoost
Algorithms 2023, 16(6), 271; https://doi.org/10.3390/a16060271 - 29 May 2023
Cited by 2 | Viewed by 4410
Abstract
Network analysis aids management in reducing overall expenditures and maintenance workload. Social media platforms frequently use neural networks to suggest material that corresponds with user preferences. Machine learning is one of many methods for social network analysis. Machine learning algorithms operate on a [...] Read more.
Network analysis aids management in reducing overall expenditures and maintenance workload. Social media platforms frequently use neural networks to suggest material that corresponds with user preferences. Machine learning is one of many methods for social network analysis. Machine learning algorithms operate on a collection of observable features that are taken from user data. Machine learning and neural network-based systems represent a topic of study that spans several fields. Computers can now recognize the emotions behind particular content uploaded by users to social media networks thanks to machine learning. This study examines research on machine learning and neural networks, with an emphasis on social analysis in the context of the current literature. Full article
(This article belongs to the Special Issue Machine Learning in Social Network Analytics)
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