Challenges and Perspectives of Social Networks within Social Computing

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 31 August 2024 | Viewed by 18139

Special Issue Editors


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Guest Editor
National Research Council, Institute of Research on Population and Social Policies (CNR-IRPPS), 00185 Rome, Italy
Interests: social networks; machine learning; human–computer interaction; knowledge management; knowledge sharing; emotion; social informatics

E-Mail Website
Guest Editor
National Research Council, Institute of Research on Population and Social Policies (CNR-IRPPS), 00185 Rome, Italy
Interests: social informatics; social computing; human-machine interaction; multimodal interaction; sketch-based interfaces; multimedia applications; user modelling; knowledge bases; spatial data; geographic information systems; responsible research and innovation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
National Research Council, Institute of Research on Population and Social Policies (CNR-IRPPS), 00185 Rome, Italy
Interests: social informatics; social computing; data and knowledge bases; human-machine interaction; user-machine natural interaction; user modelling; visual interaction; sketch-based interfaces; geographic information systems; medical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social networks allow people to connect with each other and express their thoughts, opinions and emotions, sharing content in different forms (documents, photos, videos, etc.).

This implies a pervasive use of social networks that makes available very large amounts of data on conversations, text, audio and video (i.e., multimodal data) that are significant due to their huge sizes, the variety of their topics and the dynamism of the language used.

This significant amount of data gives rise to the need of addressing both technological and social challenges. In particular, the availability of large amounts of data can be used for addressing emergent challenges (ranging from fake news to security) using different methodologies, technologies and tools related to topic areas such as human–computer interaction, social media and AI, social networks and big data, virtual reality and augmented reality environments, e-learning environments, Internet of Things, security, entertainment, video indexing and retrieval and monitoring systems, and to systems for supporting communication within crisis scenarios (public emergencies such as pandemics, wars or earthquakes).

These challenges can be addressed by applying methods and tools including machine learning, deep learning, emotion recognition, fake news detection, pattern recognition, semantic knowledge discovery, social network mining, text mining, multimedia data mining, and social and educational studies.

The purpose of this Special Issue is to discuss the role of social networks within challenges and perspectives in the different areas of application. Therefore, this Special Issue welcomes contributions of original research, advancements, developments and experiments in the following fields (not an exhaustive list):

  • Machine learning and social networks analysis;
  • Deep learning and social networks analysis;
  • Human–computer interaction and social networks;
  • Tools for augmenting social-emotional interaction;
  • Fake news and social networks;
  • Virtual reality and social networks;
  • Augmented reality and social networks;
  • Internet of Things and social networks;
  • Security and social networks;
  • Privacy and social networks;
  • Emotion recognition and social networks;
  • Social networks, emotions and social phenomena;
  • Impact of social networks;
  • Social interaction and social networks;
  • E-learning and social networks.

Dr. Maria Chiara Caschera
Dr. Patrizia Grifoni
Dr. Fernando Ferri
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. Big Data and Cognitive Computing 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

  • machine learning and social networks analysis
  • deep learning and social networks analysis
  • emotion recognition and social networks
  • human–computer interaction and social network
  • fake news and social networks
  • virtual reality and social networks
  • augmented reality and social networks
  • internet of Things and social networks
  • security and social networks
  • privacy and social networks
  • datasets for social networks analysis
  • social interaction and social networks
  • social impacts of social networks
  • social phenomena and social networks

Published Papers (7 papers)

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Research

24 pages, 1607 KiB  
Article
Social Trend Mining: Lead or Lag
by Hossein Hassani, Nadejda Komendantova, Elena Rovenskaya and Mohammad Reza Yeganegi
Big Data Cogn. Comput. 2023, 7(4), 171; https://doi.org/10.3390/bdcc7040171 - 07 Nov 2023
Cited by 1 | Viewed by 1742
Abstract
This research underscores the profound implications of Social Intelligence Mining, notably employing open access data and Google Search engine data for trend discernment. Utilizing advanced analytical methodologies, including wavelet coherence analysis and phase difference, hidden relationships and patterns within social data were revealed. [...] Read more.
This research underscores the profound implications of Social Intelligence Mining, notably employing open access data and Google Search engine data for trend discernment. Utilizing advanced analytical methodologies, including wavelet coherence analysis and phase difference, hidden relationships and patterns within social data were revealed. These techniques furnish an enriched comprehension of social phenomena dynamics, bolstering decision-making processes. The study’s versatility extends across myriad domains, offering insights into public sentiment and the foresight for strategic approaches. The findings suggest immense potential in Social Intelligence Mining to influence strategies, foster innovation, and add value across diverse sectors. Full article
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21 pages, 3059 KiB  
Article
MM-EMOR: Multi-Modal Emotion Recognition of Social Media Using Concatenated Deep Learning Networks
by Omar Adel, Karma M. Fathalla and Ahmed Abo ElFarag
Big Data Cogn. Comput. 2023, 7(4), 164; https://doi.org/10.3390/bdcc7040164 - 13 Oct 2023
Cited by 1 | Viewed by 1718
Abstract
Emotion recognition is crucial in artificial intelligence, particularly in the domain of human–computer interaction. The ability to accurately discern and interpret emotions plays a critical role in helping machines to effectively decipher users’ underlying intentions, allowing for a more streamlined interaction process that [...] Read more.
Emotion recognition is crucial in artificial intelligence, particularly in the domain of human–computer interaction. The ability to accurately discern and interpret emotions plays a critical role in helping machines to effectively decipher users’ underlying intentions, allowing for a more streamlined interaction process that invariably translates into an elevated user experience. The recent increase in social media usage, as well as the availability of an immense amount of unstructured data, has resulted in a significant demand for the deployment of automated emotion recognition systems. Artificial intelligence (AI) techniques have emerged as a powerful solution to this pressing concern in this context. In particular, the incorporation of multimodal AI-driven approaches for emotion recognition has proven beneficial in capturing the intricate interplay of diverse human expression cues that manifest across multiple modalities. The current study aims to develop an effective multimodal emotion recognition system known as MM-EMOR in order to improve the efficacy of emotion recognition efforts focused on audio and text modalities. The use of Mel spectrogram features, Chromagram features, and the Mobilenet Convolutional Neural Network (CNN) for processing audio data are central to the operation of this system, while an attention-based Roberta model caters to the text modality. The methodology of this study is based on an exhaustive evaluation of this approach across three different datasets. Notably, the empirical findings show that MM-EMOR outperforms competing models across the same datasets. This performance boost is noticeable, with accuracy gains of an impressive 7% on one dataset and a substantial 8% on another. Most significantly, the observed increase in accuracy for the final dataset was an astounding 18%. Full article
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12 pages, 4099 KiB  
Article
Visual Explanations of Differentiable Greedy Model Predictions on the Influence Maximization Problem
by Mario Michelessa, Christophe Hurter, Brian Y. Lim, Jamie Ng Suat Ling, Bogdan Cautis and Carol Anne Hargreaves
Big Data Cogn. Comput. 2023, 7(3), 149; https://doi.org/10.3390/bdcc7030149 - 05 Sep 2023
Viewed by 1549
Abstract
Social networks have become important objects of study in recent years. Social media marketing has, for example, greatly benefited from the vast literature developed in the past two decades. The study of social networks has taken advantage of recent advances in machine learning [...] Read more.
Social networks have become important objects of study in recent years. Social media marketing has, for example, greatly benefited from the vast literature developed in the past two decades. The study of social networks has taken advantage of recent advances in machine learning to process these immense amounts of data. Automatic emotional labeling of content on social media has, for example, been made possible by the recent progress in natural language processing. In this work, we are interested in the influence maximization problem, which consists of finding the most influential nodes in the social network. The problem is classically carried out using classical performance metrics such as accuracy or recall, which is not the end goal of the influence maximization problem. Our work presents an end-to-end learning model, SGREEDYNN, for the selection of the most influential nodes in a social network, given a history of information diffusion. In addition, this work proposes data visualization techniques to interpret the augmenting performances of our method compared to classical training. The results of this method are confirmed by visualizing the final influence of the selected nodes on network instances with edge bundling techniques. Edge bundling is a visual aggregation technique that makes patterns emerge. It has been shown to be an interesting asset for decision-making. By using edge bundling, we observe that our method chooses more diverse and high-degree nodes compared to the classical training. Full article
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12 pages, 7237 KiB  
Article
The Value of Web Data Scraping: An Application to TripAdvisor
by Gianluca Barbera, Luiz Araujo and Silvia Fernandes
Big Data Cogn. Comput. 2023, 7(3), 121; https://doi.org/10.3390/bdcc7030121 - 21 Jun 2023
Cited by 5 | Viewed by 3007
Abstract
Social Media Analytics (SMA) is more and more relevant in today’s market dynamics. However, it is necessary to use it wisely, either in promoting any kind of product/brand, or interacting with customers. This requires its effective understanding and monitoring. One way is through [...] Read more.
Social Media Analytics (SMA) is more and more relevant in today’s market dynamics. However, it is necessary to use it wisely, either in promoting any kind of product/brand, or interacting with customers. This requires its effective understanding and monitoring. One way is through web data scraping (WDS) tools that allow to select sites and platforms to compare them in their performances. They can optimize extraction of big data published on social media. Due to current challenges, a sector that can particularly take advantage of this source is tourism (and its related sectors). This year has the hope of tourism’s revival after a pandemic whose impacts are still affecting several activities. Many traders and entrepreneurs have already used these versatile tools. However, do they really know their potential? The present study highlights the use of WDS to collect data from TripAdvisor’s social pages. Besides comparing competitors’ performance, companies also gain new knowledge of unnoticed preferences/habits. This contributes to more interesting innovations and results for them and for their customers. The approach used here is based on a project for smart tourism consultancy, from the identification of a gap in our region, to aid tourism organizations to enhance their digital presence and business model. Many things can be detected in this big source of unstructured data very quickly and easily without programming. Moreover, exploring code, either to refine the web scraper or connect it with other platforms/apps, can be an object of future research to leverage consumer behavior prediction for more advanced interactions. Full article
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26 pages, 3069 KiB  
Article
Intelligent Multi-Lingual Cyber-Hate Detection in Online Social Networks: Taxonomy, Approaches, Datasets, and Open Challenges
by Donia Gamal, Marco Alfonse, Salud María Jiménez-Zafra and Mostafa Aref
Big Data Cogn. Comput. 2023, 7(2), 58; https://doi.org/10.3390/bdcc7020058 - 24 Mar 2023
Viewed by 2325
Abstract
Sentiment Analysis, also known as opinion mining, is the area of Natural Language Processing that aims to extract human perceptions, thoughts, and beliefs from unstructured textual content. It has become a useful, attractive, and challenging research area concerning the emergence and rise of [...] Read more.
Sentiment Analysis, also known as opinion mining, is the area of Natural Language Processing that aims to extract human perceptions, thoughts, and beliefs from unstructured textual content. It has become a useful, attractive, and challenging research area concerning the emergence and rise of social media and the mass volume of individuals’ reviews, comments, and feedback. One of the major problems, apparent and evident in social media, is the toxic online textual content. People from diverse cultural backgrounds and beliefs access Internet sites, concealing and disguising their identity under a cloud of anonymity. Due to users’ freedom and anonymity, as well as a lack of regulation governed by social media, cyber toxicity and bullying speech are major issues that need an automated system to be detected and prevented. There is diverse research in different languages and approaches in this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this manuscript, a comprehensive multi-lingual and systematic review of cyber-hate sentiment analysis is presented. It states the definition, properties, and taxonomy of cyberbullying and how often each type occurs. In addition, it presents the most recent popular cyberbullying benchmark datasets in different languages, showing their number of classes (Binary/Multiple), discussing the applied algorithms, and how they were evaluated. It also provides the challenges, solutions, as well as future directions. Full article
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20 pages, 1982 KiB  
Article
Using an Evidence-Based Approach for Policy-Making Based on Big Data Analysis and Applying Detection Techniques on Twitter
by Somayeh Labafi, Sanee Ebrahimzadeh, Mohamad Mahdi Kavousi, Habib Abdolhossein Maregani and Samad Sepasgozar
Big Data Cogn. Comput. 2022, 6(4), 160; https://doi.org/10.3390/bdcc6040160 - 19 Dec 2022
Viewed by 2572
Abstract
Evidence-based policy seeks to use evidence in public policy in a systematic way in a bid to improve decision-making quality. Evidence-based policy cannot work properly and achieve the expected results without accurate, appropriate, and sufficient evidence. Given the prevalence of social media and [...] Read more.
Evidence-based policy seeks to use evidence in public policy in a systematic way in a bid to improve decision-making quality. Evidence-based policy cannot work properly and achieve the expected results without accurate, appropriate, and sufficient evidence. Given the prevalence of social media and intense user engagement, the question to ask is whether the data on social media can be used as evidence in the policy-making process. The question gives rise to the debate on what characteristics of data should be considered as evidence. Despite the numerous research studies carried out on social media analysis or policy-making, this domain has not been dealt with through an “evidence detection” lens. Thus, this study addresses the gap in the literature on how to analyze the big text data produced by social media and how to use it for policy-making based on evidence detection. The present paper seeks to fill the gap by developing and offering a model that can help policy-makers to distinguish “evidence” from “non-evidence”. To do so, in the first phase of the study, the researchers elicited the characteristics of the “evidence” by conducting a thematic analysis of semi-structured interviews with experts and policy-makers. In the second phase, the developed model was tested against 6-month data elicited from Twitter accounts. The experimental results show that the evidence detection model performed better with decision tree (DT) than the other algorithms. Decision tree (DT) outperformed the other algorithms by an 85.9% accuracy score. This study shows how the model managed to fulfill the aim of the present study, which was detecting Twitter posts that can be used as evidence. This study contributes to the body of knowledge by exploring novel models of text processing and offering an efficient method for analyzing big text data. The practical implication of the study also lies in its efficiency and ease of use, which offers the required evidence for policy-makers. Full article
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29 pages, 620 KiB  
Article
A Space-Time Framework for Sentiment Scope Analysis in Social Media
by Gianluca Bonifazi, Francesco Cauteruccio, Enrico Corradini, Michele Marchetti, Luigi Sciarretta, Domenico Ursino and Luca Virgili
Big Data Cogn. Comput. 2022, 6(4), 130; https://doi.org/10.3390/bdcc6040130 - 03 Nov 2022
Cited by 19 | Viewed by 2694
Abstract
The concept of scope was introduced in Social Network Analysis to assess the authoritativeness and convincing ability of a user toward other users on one or more social platforms. It has been studied in the past in some specific contexts, for example to [...] Read more.
The concept of scope was introduced in Social Network Analysis to assess the authoritativeness and convincing ability of a user toward other users on one or more social platforms. It has been studied in the past in some specific contexts, for example to assess the ability of a user to spread information on Twitter. In this paper, we propose a new investigation on scope, as we want to assess the scope of the sentiment of a user on a topic. We also propose a multi-dimensional definition of scope. In fact, besides the traditional spatial scope, we introduce the temporal one, which has never been addressed in the literature, and propose a model that allows the concept of scope to be extended to further dimensions in the future. Furthermore, we propose an approach and a related set of parameters for measuring the scope of the sentiment of a user on a topic in a social network. Finally, we illustrate the results of an experimental campaign we conducted to evaluate the proposed framework on a dataset derived from Reddit. The main novelties of this paper are: (i) a multi-dimensional view of scope; (ii) the introduction of the concept of sentiment scope; (iii) the definition of a general framework capable of analyzing the sentiment scope related to any subject on any social network. Full article
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