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Electronics
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Application of Data Mining in Social Media
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Application of Data Mining in Social Media

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 6611

Special Issue Editors

Dr. Junaid Rashid

E-Mail Website
Guest Editor
Department of Data Science, Sejong University, Seoul 05006, Republic of Korea
Interests: data science; topic modeling; text mining; information retrieval; machine learning; natural language processing; big data analysis; data mining; artificial intelligence
Prof. Dr. Patrick Siarry

E-Mail Website
Guest Editor
University Paris-Est Creteil Images, Signals and Intelligent Systems Laboratory 61 avenue du Général de Gaulle, 94010 Creteil, France
Interests: artificial intelligence; soft grid; Internet of Things

Special Issue Information

Dear Colleagues,

The growing interest of people around the globe in social networking sites, streaming platforms, and other digital platforms has made the Internet a necessary tool for everyday tasks such as commerce, schooling, leisure activities, and interaction. Nowadays, individuals using the internet have almost limitless accessibility to distribute content. This creates an excellent chance to utilize this beneficial data by turning it into knowledge using the right methods. In such a scenario, data mining techniques become powerful instruments for assisting consumers in finding the most appropriate online content, goods, or services by investigating a variety of social media factors, including user behaviour, communities, network topologies, informational dispersion, and a lot more. However, the vast volumes of social networking information and the extremely complicated and constantly changing social behaviour of consumers have resulted in the development of massive quantities of high-dimension, unreliable, ambiguous, and noisy data from such platforms. Consequently, demonstrating and analysing this enormous ambiguity of electronic content and offering excellent services to customers seems to be very difficult.

Soft computing approaches (i.e., fuzzy logic, machine learning, deep learning, etc.) can play a considerably vital part in tackling the above-mentioned issues because of their ability to cope with data unpredictability and ambiguity. These approaches are not only used in traditional social media analysis but also show effectiveness in distinct areas such as the detection of hate speech, misinformation, sentiment analysis, and abusive behaviour. The topics of interest include, but are not limited to, the following:

  1. Social media analysis using data mining;
  2. Machine learning models for data mining;
  3. Deep learning models for data mining;
  4. The intersection of computer vision and artificial intelligence with data mining;
  5. Soft computing and modelling in data mining;
  6. Applications of data mining in hate speech detection;
  7. Misinformation and abusive behaviour detection using data mining;
  8. Sentiment analysis techniques in social media using data mining;
  9. Data mining approaches for social media in healthcare;
  10. Role of data mining in analysing user behaviour on social media platforms.

This Special Issue offers an opportunity for scientists and professionals from computer science, data mining, ubiquitous computing, and social sites to exchange concepts, novel solutions, and strategies for advancing the smart analysis of data and online handling of data. We invite the submission of unpublished, original work that applies any advanced techniques and methodologies to all areas around the subject matter of this Special Issue.

Dr. Junaid Rashid
Prof. Dr. Patrick Siarry
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • data mining
  • social media analysis
  • soft computing
  • network science
  • natural language processing
  • text mining
  • information retrieval
  • computational intelligence
  • sentiment analysis
  • machine learning
  • healthcare data mining
  • hate speech detection
  • misinformation detection
  • abusive behavior detection

Published Papers (4 papers)

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Research

17 pages, 1683 KiB  
Article
Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus
by Ronghao Pan, José Antonio García-Díaz and Rafael Valencia-García
Electronics 2024, 13(4), 717; https://doi.org/10.3390/electronics13040717 - 9 Feb 2024
Viewed by 611
Abstract
Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical [...] Read more.
Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%. Full article
(This article belongs to the Special Issue Application of Data Mining in Social Media)
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19 pages, 7943 KiB  
Article
Recommendations for Responding to System Security Incidents Using Knowledge Graph Embedding
by HyoungJu Kim and Junho Choi
Electronics 2024, 13(1), 171; https://doi.org/10.3390/electronics13010171 - 30 Dec 2023
Cited by 1 | Viewed by 693
Abstract
Recently, security attacks occurring in edge computing environments have emerged as an important research topic in the field of cybersecurity. Edge computing is a distributed computing technology that expands the existing cloud computing architecture to introduce a new layer, the edge layer, between [...] Read more.
Recently, security attacks occurring in edge computing environments have emerged as an important research topic in the field of cybersecurity. Edge computing is a distributed computing technology that expands the existing cloud computing architecture to introduce a new layer, the edge layer, between the cloud layer and the user terminal layer. Edge computing has the advantage of greatly improving the data processing speed and efficiency but, at the same time, is complex, and various new attacks occur frequently. Therefore, for improving the security of edge computing, effective and intelligent security strategies and policies must be established in consideration of a wide range of vulnerabilities. Intelligent security systems, which have recently been studied, provide a way to detect and respond to security threats by integrating the latest technologies, such as machine learning and big data analysis. Intelligent security technology can quickly recognize attack patterns or abnormal behaviors within a large amount of data and continuously respond to new threats through learning. In particular, knowledge-based technologies using ontology or knowledge graph technology play an important role in more deeply understanding the meaning and relationships between of security data and more effectively detecting and responding to complex threats. This study proposed a method for recommending strategies to respond to edge computing security incidents based on the automatic generation and embedding of security knowledge graphs. An EdgeSecurity–BERT model, utilizing the latest security vulnerability data from edge computing, was designed to extract entities and their relational information. Also, a security vulnerability assessment method was proposed to recommend strategies to respond to edge computing security incidents through knowledge graph embedding. In the experiment, the classification accuracy of security news data for common vulnerability and exposure data was approximately 86% on average. In addition, the EdgeSecurityKG applying the security vulnerability similarity improved the Hits@10 performance to identify the correct link, but the MR performance was degraded owing to the increased complexity. In complex areas, such as security, careful evaluation of the model’s performance and data selection are important. The EdgeSecurityKG applying the security vulnerability similarity provides an important advantage in understanding complex security vulnerability relationships. Full article
(This article belongs to the Special Issue Application of Data Mining in Social Media)
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13 pages, 1111 KiB  
Article
Predicting Loneliness through Digital Footprints on Google and YouTube
by Eiman Ahmed, Liyang Xue, Aniket Sankalp, Haein Kong, Arcadio Matos, Vincent Silenzio and Vivek K. Singh
Electronics 2023, 12(23), 4821; https://doi.org/10.3390/electronics12234821 - 29 Nov 2023
Viewed by 2838
Abstract
Loneliness is an increasingly prevalent condition with many adverse effects on health and quality of life. Accordingly, there is a growing interest in developing automated or low-cost methods for triaging and supporting individuals encountering psychosocial distress. This study marks an early attempt at [...] Read more.
Loneliness is an increasingly prevalent condition with many adverse effects on health and quality of life. Accordingly, there is a growing interest in developing automated or low-cost methods for triaging and supporting individuals encountering psychosocial distress. This study marks an early attempt at building predictive models to detect loneliness automatically using the digital traces of individuals’ online behavior (Google search and YouTube consumption). Based on a longitudinal study with 92 adult participants for eight weeks in 2021, we find that users’ online behavior can help create automated classification tools for loneliness with high accuracy. Furthermore, we observed behavioral differences in digital traces across platforms. The “not lonely” participants had higher aggregated YouTube activity and lower aggregated Google search activity than “lonely” participants. Our results indicate the need for a further platform-aware exploration of technology use for studies interested in developing automated assessment tools for psychological well-being. Full article
(This article belongs to the Special Issue Application of Data Mining in Social Media)
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23 pages, 4391 KiB  
Article
TSM-CV: Twitter Sentiment Analysis for COVID-19 Vaccines Using Deep Learning
by Saleh Albahli and Marriam Nawaz
Electronics 2023, 12(15), 3372; https://doi.org/10.3390/electronics12153372 - 7 Aug 2023
Cited by 3 | Viewed by 1380
Abstract
The coronavirus epidemic has imposed a devastating impact on humans around the globe, causing profound anxiety, fear, and complex emotions and feelings. Vaccination against the new coronavirus has started, and people’s feelings are becoming more diverse and complicated. In the presented work, our [...] Read more.
The coronavirus epidemic has imposed a devastating impact on humans around the globe, causing profound anxiety, fear, and complex emotions and feelings. Vaccination against the new coronavirus has started, and people’s feelings are becoming more diverse and complicated. In the presented work, our goal is to use the deep learning (DL) technique to understand and elucidate their feelings. Due to the advancement of IT and internet facilities, people are socially connected to explain their emotions and sentiments. Among all social sites, Twitter is the most used platform among consumers and can assist scientists to comprehend people’s opinions related to anything. The major goal of this work is to understand the audience’s varying sentiments about the vaccination process by using data from Twitter. We have employed both the historic (All COVID-19 Vaccines Tweets Kaggle dataset) and real (tweets) data to analyze the people’s sentiments. Initially, a preprocessing step is applied to the input samples. Then, we use the FastText approach for computing semantically aware features. In the next step, we apply the Valence Aware Dictionary for sentiment Reasoner (VADER) method to assign the labels to the collected features as being positive, negative, or neutral. After this, a feature reduction step using the Non-Negative Matrix Factorization (NMF) approach is utilized to minimize the feature space. Finally, we have used the Random Multimodal Deep Learning (RMDL) classifier for sentiment prediction. We have confirmed through experimentation that our work is effective in examining the emotions of people toward the COVID-19 vaccines. The presented work has acquired an accuracy result of 94.81% which is showing the efficacy of our strategy. Other standard measures like precision, recall, F1-score, AUC, and confusion matrix are also reported to show the significance of our work. The work is aimed to improve public understanding of coronavirus vaccines which can help the health departments to stop the anti-vaccination leagues and motivate people to a booster dose of coronavirus. Full article
(This article belongs to the Special Issue Application of Data Mining in Social Media)
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