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The Social Relation Mediated by New Technology: Behavior, Psychology and Society

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Psychology of Sustainability and Sustainable Development".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6337

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Special Issue Editors

Dr. Stefano Ruggieri

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Guest Editor

Faculty of Human and Social Sciences, Kore University of Enna, 94100 Enna, Italy
Interests: social media; online social relations; computer mediated communication; language on SNSs

Dr. Alessia Passanisi

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Guest Editor

Faculty of Human and Social Sciences, Kore University of Enna, 94100 Enna, Italy
Interests: concepts and categorization; behavioral addictions; personality
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Special Issue Information

Dear Colleagues,

The world is constantly changing due to the pervasive use of the world wide web that currently shapes our lives and our society. Nowadays, more than 4.5 billion people access the Internet regularly, which corresponds to about 60% of the global population. At the same time, active users on social network sites number approximately 4.14 billion. The best-known and most widespread SNS, Facebook, counted about 69 million active users worldwide in early 2008, whereas in early 2021 the number had grown to more than 2.7 billion. Moreover, most young people, and not only the young, are constantly connected to the Internet and are ready to look at their smartphones when they receive a notification about their online connections’ recent posts.

Much of the time on SNSs is spent looking at the stream of information updated all day and all night by members of our social networks. Friends, relatives, acquaintances and people we have never met appear with their posts on our Facebook timeline, in our Instagram news feed or in our WhatsApp status updates with their life, travels, friendships, political ideas, parties, new jobs, and romantic relationships.

In most cases, new social media technologies offer unique opportunities to interact with others, i.e., maintaining and improving networks of real friendship, fostering social capital, facilitating support exchange and engaging in peer-to-peer comparisons, with self-presentation and self-confidence related to SNSs as elements contributing to the definition of one’s identity. In other cases, an impressive amount of research shows the potential risks of use and abuse of new social media in educational, workplaces, social context (i.e., addictions, distress, and concern over negative evaluation).

The main aim of this Special Issue is to promote knowledge of the positive and negative aspects of new media in social relations with the porpoise of promoting a social sustainability of human beings on new technologies.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Social network sites interactions;
Problematic media use;
Augmented reality;
Fear of missing out;
Social comparison;
Social interaction in workplaces;
Body dissatisfaction in new technologies;
Social media relation in educational settings;
Sexting;
Cyberbullying;
Language;
Entrepreneurship;
Leadership;
Online intervention.

We look forward to receiving your contributions.

Dr. Stefano Ruggieri
Dr. Alessia Passanisi
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. Sustainability 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

social media
social network sites
social interactions
e-entrepreneurship
sexting
cyberbullying
social media education
problematic social media use
Internet addiction

Published Papers (4 papers)

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Research

15 pages, 967 KiB

Open AccessArticle

Social Comparisons and Compensatory Consumption: The Art of Buying a Superior Self

by Kristi Reid-Partin and Veena Chattaraman

Sustainability 2023, 15(22), 15950; https://doi.org/10.3390/su152215950 - 15 Nov 2023

Viewed by 966

Abstract

This paper examines how consumers’ body satisfaction, beliefs about the malleability of their appearance, and incidental comparisons with upward vs. lateral social media influencers interact to affect the type of consumption behaviors they engage in. Based on propositions of the compensatory consumption behavior (CCB) model and the social comparison theory, this study employs an online experiment with a 2 (social comparison: upward/lateral) × 2 (body satisfaction: low/high) × 2 (implicit theory: entity/incremental) × 2 (product type: head- and body-related) mixed-factorial design among a sample of 192 women (19–35 years). The appearance of the influencers was manipulated (upward: thinner, primped models; lateral: average weight, natural models), as were the products being advertised, whereas body satisfaction and consumers’ implicit theory were measured. The results indicated that consumers were more persuaded to purchase products from lateral compared to upward influencers. Further, lateral influencers were more persuasive for head-related (vs. body-related) products, whereas upward influencers were more successful in promoting body-related (vs. head-related) products. A significant (p < 0.05) interaction between body satisfaction, implicit theory of appearance, and product type also emerged, supporting the proposals of the CCB model on how consumption behaviors are affected by felt discrepancies. These findings indicate that marketers can access more effective marketing results by collaborating with influencers that have a similar appearance to that of their target audience. Full article

(This article belongs to the Special Issue The Social Relation Mediated by New Technology: Behavior, Psychology and Society)

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15 pages, 3193 KiB

Open AccessArticle

Running a Sustainable Social Media Business: The Use of Deep Learning Methods in Online-Comment Short Texts

by Weibin Lin, Qian Zhang, Yenchun Jim Wu and Tsung-Chun Chen

Sustainability 2023, 15(11), 9093; https://doi.org/10.3390/su15119093 - 05 Jun 2023

Viewed by 1019

Abstract

With the prevalence of the Internet in society, social media has considerably altered the ways in which consumers conduct their daily lives and has gradually become an important channel for online communication and sharing activities. At the same time, whoever can rapidly and accurately disseminate online data among different companies affects their sales and competitiveness; therefore, it is urgent to obtain consumer public opinions online via an online platform. However, problems, such as sparse features and semantic losses in short-text online reviews, exist in the industry; therefore, this article uses several deep learning techniques and related neural network models to analyze Weibo online-review short texts to perform a sentiment analysis. The results show that, compared with the vector representation generated by Word2Vec’s CBOW model, BERT’s word vectors can obtain better sentiment analysis results. Compared with CNN, BiLSTM, and BiGRU models, the improved BiGRU-Att model can effectively improve the accuracy of the sentiment analysis. Therefore, deep learning neural network systems can improve the quality of the sentiment analysis of short-text online reviews, overcome the problems of the presence of too many unfamiliar words and low feature density in short texts, and provide an efficient and convenient computational method for improving the ability to perform sentiment analysis of short-text online reviews. Enterprises can use online data to analyze and immediately grasp the intentions of existing or potential consumers towards the company or product through deep learning methods and develop new services or sales plans that are more closely related to consumers to increase competitiveness. When consumers experience the use of new services or products again, they may provide feedback online. In this situation, companies can use deep learning sentiment analysis models to perform additional analyses, forming a dynamic cycle to ensure the sustainable operation of their enterprises. Full article

(This article belongs to the Special Issue The Social Relation Mediated by New Technology: Behavior, Psychology and Society)

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13 pages, 459 KiB

Open AccessArticle

The Effects of Leader Self-Sacrifice in Virtual Teams on Prosocial Behavior: The Mediational Role of Team Identification and Self-Efficacy

by Stefano Ruggieri, Melissa Gagliano, Rocco Servidio, Ugo Pace and Alessia Passanisi

Sustainability 2023, 15(7), 6098; https://doi.org/10.3390/su15076098 - 31 Mar 2023

Cited by 2 | Viewed by 1539

Abstract

Leadership is one of the most studied features of virtual teams. Among the various characteristics analyzed by recent literature, leadership self-sacrifice is one of the most important, as it represents a predictor of many positive characteristics of teams’ functioning. In this study, we (a) analyze the relationship between leader self-sacrifice and the prosocial behavior of followers in a work team and (b) observe the effects of leader self-sacrifice in virtual teams. A sample of 197 university students enrolled in a psychology course took part in a group electronic task of writing a detailed research plan for a scientific investigation. Participants collaborated in groups of five, led by a senior student for 30 days. Results showed the presence of an effect of e-leadership self-sacrifice on followers’ prosocial behavior. Another effect of e-leadership self-sacrifice was found via team identification and perceived self-efficacy. Findings are discussed on the basis of Social Identity Theory, showing the importance of self-sacrifice e-leaders to promote reciprocal prosocial behavior of the followers. Full article

(This article belongs to the Special Issue The Social Relation Mediated by New Technology: Behavior, Psychology and Society)

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16 pages, 630 KiB

Open AccessArticle

How to Detect Online Hate towards Migrants and Refugees? Developing and Evaluating a Classifier of Racist and Xenophobic Hate Speech Using Shallow and Deep Learning

by Carlos Arcila-Calderón, Javier J. Amores, Patricia Sánchez-Holgado, Lazaros Vrysis, Nikolaos Vryzas and Martín Oller Alonso

Sustainability 2022, 14(20), 13094; https://doi.org/10.3390/su142013094 - 13 Oct 2022

Cited by 7 | Viewed by 2097

Abstract

Hate speech spreading online is a matter of growing concern since social media allows for its rapid, uncontrolled, and massive dissemination. For this reason, several researchers are already working on the development of prototypes that allow for the detection of cyberhate automatically and on a large scale. However, most of them are developed to detect hate only in English, and very few focus specifically on racism and xenophobia, the category of discrimination in which the most hate crimes are recorded each year. In addition, ad hoc datasets manually generated by several trained coders are rarely used in the development of these prototypes since almost all researchers use already available datasets. The objective of this research is to overcome the limitations of those previous works by developing and evaluating classification models capable of detecting racist and/or xenophobic hate speech being spread online, first in Spanish, and later in Greek and Italian. In the development of these prototypes, three differentiated machine learning strategies are tested. First, various traditional shallow learning algorithms are used. Second, deep learning is used, specifically, an ad hoc developed RNN model. Finally, a BERT-based model is developed in which transformers and neural networks are used. The results confirm that deep learning strategies perform better in detecting anti-immigration hate speech online. It is for this reason that the deep architectures were the ones finally improved and tested for hate speech detection in Greek and Italian and in multisource. The results of this study represent an advance in the scientific literature in this field of research, since up to now, no online anti-immigration hate detectors had been tested in these languages and using this type of deep architecture. Full article

(This article belongs to the Special Issue The Social Relation Mediated by New Technology: Behavior, Psychology and Society)

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