Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.8
Journals
Computers
Special Issues
Harnessing Artificial Intelligence for Social and Semantic Understanding
share announcement

Harnessing Artificial Intelligence for Social and Semantic Understanding

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 946

Special Issue Editors

Dr. Yorghos Voutos

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece
Interests: IoT; data and social mining; environmental monitoring; semantics; knowledge representation; spatio-temporal
Dr. Akrivi Krouska

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: social net artificial intellworking services; sentiment analysis; learning analytics; user modeling andigence
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Troussas

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: adaptive hypermedia systems; artificial intelligence in education; human-centered artificial intelligence; human–computer interaction; personalized software
Special Issues, Collections and Topics in MDPI journals
Dr. Phivos Mylonas

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cleo Sgouropoulou

E-Mail Website
Guest Editor
Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: e-learning; open educational resources (OERs); learning outcomes; competence and skill modeling for strengthening LLL; mobility and training to employment pathways; open data and analytics for e-research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's data-rich landscape, the demand for efficient artificial intelligence (AI) models, tools, and applications for data mining and machine learning is more pronounced than ever. The proliferation of social and semantic networks has generated a wealth of valuable information, presenting both opportunities and challenges. Researchers grapple with novel approaches to address emerging research questions, particularly in the realms of social information processing and semantic applications. The proposed Special Issue and the SMAP workshop series revolve around two central themes: first, the extraction, processing, and analysis of data, information, and knowledge using AI techniques; and second, the application of these results to construct effective models and tools. Ultimately, this interdisciplinary endeavor aims to foster diverse behaviors associated with AI activities, bridging the gap between social and semantic impact. Computer scientists are encouraged to contribute innovative AI solutions to tackle the inherent challenges posed by dynamic and semantically heterogeneous computational data. The call for relevant research manuscripts is open to all interested parties.

The content of the proposed Special Issue and the goals of the SMAP workshop series are organized around two main themes. The first theme focuses on efficiently extracting, processing, manipulating and analysing data, information and knowledge with the utilization of artificial intelligence in the process, while the second theme focuses on using the above results to effectively build models, tools and applications. The ultimate goal, of course, is to promote a variety of machine and/or human behaviours associated with related artificial intelligence activities.

In addition to the Open Call, selected papers that will be presented during SMAP 2024 will be invited to be submitted as extended versions to this Special Issue. In this case, the workshop paper should be cited and noted on the first page of the submitted paper; authors are asked to disclose that it is a workshop paper in their Cover Letter and include a statement on what has been changed compared to the original workshop paper. Each submission to this journal Special Issue should contain at least 50% new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.

All submitted papers will undergo standard peer-review procedures. Accepted papers will be published in open access format in Computers and available on the Special Issue’s website.

Dr. Yorghos Voutos
Dr. Akrivi Krouska
Dr. Christos Troussas
Dr. Phivos Mylonas
Prof. Dr. Cleo Sgouropoulou
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. Computers 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 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.

Keywords

  • artificial intelligence (AI)
  • social network
  • semantic network

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 601 KiB  
Article
Harnessing Machine Learning to Unveil Emotional Responses to Hateful Content on Social Media
by Ali Louati, Hassen Louati, Abdullah Albanyan, Rahma Lahyani, Elham Kariri and Abdulrahman Alabduljabbar
Computers 2024, 13(5), 114; https://doi.org/10.3390/computers13050114 - 29 Apr 2024
Viewed by 285
Abstract
Within the dynamic realm of social media, the proliferation of harmful content can significantly influence user engagement and emotional health. This study presents an in-depth analysis that bridges diverse domains, from examining the aftereffects of personal online attacks to the intricacies of online [...] Read more.
Within the dynamic realm of social media, the proliferation of harmful content can significantly influence user engagement and emotional health. This study presents an in-depth analysis that bridges diverse domains, from examining the aftereffects of personal online attacks to the intricacies of online trolling. By leveraging an AI-driven framework, we systematically implemented high-precision attack detection, psycholinguistic feature extraction, and sentiment analysis algorithms, each tailored to the unique linguistic contexts found within user-generated content on platforms like Reddit. Our dataset, which spans a comprehensive spectrum of social media interactions, underwent rigorous analysis employing classical statistical methods, Bayesian estimation, and model-theoretic analysis. This multi-pronged methodological approach allowed us to chart the complex emotional responses of users subjected to online negativity, covering a spectrum from harassment and cyberbullying to subtle forms of trolling. Empirical results from our study reveal a clear dose–response effect; personal attacks are quantifiably linked to declines in user activity, with our data indicating a 5% reduction after 1–2 attacks, 15% after 3–5 attacks, and 25% after 6–10 attacks, demonstrating the significant deterring effect of such negative encounters. Moreover, sentiment analysis unveiled the intricate emotional reactions users have to these interactions, further emphasizing the potential for AI-driven methodologies to promote more inclusive and supportive digital communities. This research underscores the critical need for interdisciplinary approaches in understanding social media’s complex dynamics and sheds light on significant insights relevant to the development of regulation policies, the formation of community guidelines, and the creation of AI tools tailored to detect and counteract harmful content. The goal is to mitigate the impact of such content on user emotions and ensure the healthy engagement of users in online spaces. Full article
(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)
►▼ Show Figures

Figure 1

14 pages, 1352 KiB  
Article
MTL-AraBERT: An Enhanced Multi-Task Learning Model for Arabic Aspect-Based Sentiment Analysis
by Arwa Fadel, Mostafa Saleh, Reda Salama and Osama Abulnaja
Computers 2024, 13(4), 98; https://doi.org/10.3390/computers13040098 - 15 Apr 2024
Viewed by 470
Abstract
Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis; it works on an aspect level. It mainly focuses on extracting aspect terms from text or reviews, categorizing the aspect terms, and classifying the sentiment polarities toward each aspect term and aspect [...] Read more.
Aspect-based sentiment analysis (ABSA) is a fine-grained type of sentiment analysis; it works on an aspect level. It mainly focuses on extracting aspect terms from text or reviews, categorizing the aspect terms, and classifying the sentiment polarities toward each aspect term and aspect category. Aspect term extraction (ATE) and aspect category detection (ACD) are interdependent and closely associated tasks. However, the majority of the current literature on Arabic aspect-based sentiment analysis (ABSA) deals with these tasks individually, assumes that aspect terms are already identified, or employs a pipeline model. Pipeline solutions employ single models for each task, where the output of the ATE model is utilized as the input for the ACD model. This sequential process can lead to the propagation of errors across different stages, as the performance of the ACD model is influenced by any errors produced by the ATE model. Therefore, the primary objective of this study was to investigate a multi-task learning approach based on transfer learning and transformers. We propose a multi-task learning model (MTL) that utilizes the pre-trained language model (AraBERT), namely, the MTL-AraBERT model, for extracting Arabic aspect terms and aspect categories simultaneously. Specifically, we focused on training a single model that simultaneously and jointly addressed both subtasks. Moreover, this paper also proposes a model integrating AraBERT, single pair classification, and BiLSTM/BiGRU that can be applied to aspect term polarity classification (APC) and aspect category polarity classification (ACPC). All proposed models were evaluated using the SemEval-2016 annotated dataset for the Arabic hotel dataset. The experiment results of the MTL model demonstrate that the proposed models achieved comparable or better performance than state-of-the-art works (F1-scores of 80.32% for the ATE and 68.21% for the ACD). The proposed SPC-BERT model demonstrated high accuracy, reaching 89.02% and 89.36 for APC and ACPC, respectively. These improvements hold significant potential for future research in Arabic ABSA. Full article
(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop