Social Networking Applications Research Articles

Social network analysis and application for ecosystem service perceptions by stakeholders for coastal wetland conservation

Stakeholders play a dominant role in shaping ecosystems and driving current environmental management; thus, social networks of stakeholders should be cleared up for ecosystem governance to maintain the ecosystem services and sustainable development. Based on the field survey and questionnaires, we constructed a social network framework for coastal wetland conservation and took the Yellow River Delta (YRD) as an example. We further explored the stakeholder network structure and function and took ecosystem service perceptions as an application by stakeholders impacting coastal wetland conservation. In this case study, we identified a non-cohesive network with low density and high centrality. Among all these stakeholders, provincial government (PGov), municipal government (MGov), and educational institutions (Edu) have high module-to-module participation (MP) scores with other organizations, indicating that they connect others tightly in the network. The main relationships between different stakeholder groups were concentrated in information sharing (79%), production-consumption (14%), and administrative management (7%) relationships, of which the information sharing relationships have a stronger correlation with productivity in the Quadratic Assignment Procedure (QAP) test. We also found that support and regulation services were of the most concern for the ecosystem services perception of stakeholders. Based on our findings, improving the closeness among relevant stakeholder groups, optimizing their overall relationships, and enhancing the protection of ecosystem services are greatly needed to benefit ecosystem conservation.

Investigating Characteristics of Learning Environments During the COVID-19 Pandemic: A Systematic Review

Dramatic change in learning environments during the COVID-19 pandemic highlighted the significance of virtual learning and led to more interactive learning environments. Quick adoption of online and social interactive learning in many universities around the world raised challenges and emphasized the importance of investigating different learning environments. This paper investigates the accelerated transition in education from traditional learning environments through online learning environments to social innovative learning environments, and the latest trends of this change. The stages of transition were divided into three parts: before, during, and after the COVID-19 pandemic, which was the reason for this accelerated change. Features and characteristics of each stage of transition were analyzed and discussed, based on the following factors: edu-space and classrooms, the learning and teaching process, curricular choices, information and communication technology applications, students’ and educators’ perceptions, edu-approaches, and knowledge transformation. A systematic review approach was used to investigate learning environments based on the literature reviews of previous publications. Analysis of these features revealed the main characteristics and differences in each stage. New trends in online learning environments and social innovative learning environments were identified including cloud platforms, massive open online courses, digital learning management systems, open educational resources, open educational practices, m-learning, and social network applications. Finally, this study makes two recommendations: 1) the adoption of online learning environments and social innovative learning environment applications to continue the e-learning process during the pandemic, and 2) the enhanced usage of online learning environments and social innovative learning environment applications in the future by educational institutions and governments.

Device Information for Forensic Analysis

This document provides an overview of information for forensic analysis related to device name, serial number and model of devices used to connect to social networking platforms. The increasing use of social networking applications on smartphones makes these devices a gold mine for forensic researchers. Potential evidence can be captured on these devices and recovered with the right tools and research methods. The increasing proliferation of network devices in homes and buildings increases the possibilities of finding digital traces relevant to an investigation, physical or virtual: cyber-attacks, identity theft, etc. connected to the network can also find useful traces on the devices themselves found or stored in an associated cloud account that can be identified by device ID, model, and serial number. Keywords: Digital Forensics; Device Information, Evidence, Storage, Cyber forensics framework. BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security & Forensics. Open Access. Distributed Free Citation: Paul Antwi (2022): Device Information for Forensic Analysis Book Chapter Series on Research Nexus in IT, Law, Cyber Security & Forensics. Pp 373-378 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P59

Predictors of COVID-19-related health anxiety among health care workers: a cross-sectional study

BackgroundOne of the psychological issues that may affect health care workers (HCWs) during the outbreak of COVID-19 is health anxiety. Health anxiety disorder goes beyond normal health concerns and can seriously affect occupational and interpersonal performance. The present study was designed to determine the level of COVID-19-related health anxiety and its predictors in Iranian HCWs.MethodsThis is a cross-sectional study. Data were collected online through a demographic information questionnaire and the short version of the Health Anxiety Questionnaire. The online questionnaires were created via Google Form and the URL link was sent to HCWs via email or social networking applications. In total, questionnaires were sent to more than 1,500 HCWs throughout Iran. Data were analyzed with SPSS software version 23.ResultsFive hundred and fifty-two HCWs completed and sent the questionnaires. The mean scores of health anxiety of HCWs were higher than the cutoff point of health anxiety (17.28 ± 8.84) and 58.1% of HCWs had health anxiety. There was a significant inverse relationship between health anxiety score and age (r = − 0.13; P = 0.002), work experience (r = − 0.16; P < 0.001) and income level (r = − 0.097; P = 0.03). The rate of health anxiety was significantly higher in females (P = 0.03). Based on regression results, age and hospital category were significant risk factors for health anxiety.ConclusionBased on the results of this study, employees working in health centers in Iran had high health anxiety. Due to the high level of health anxiety in HCWs, it is important to consider strategies to reduce their health anxiety in the current situation.

Effect of oral health promotion interventions on pregnant women dental caries: a field trial

BackgroundDental caries is a costly and very common disease, especially in pregnant women. Reasons such as not paying attention to oral health, poor diet and also lack of adequate education in this regard cause this to happen. Performing well-designed educational interventions using primary health system’s forces, can improve oral health of pregnant women and help control this disease. We conducted this study to evaluate the effectiveness of some oral health interventions on pregnant women dental caries.MethodsA field trial study was done in comprehensive Health Centers in Varamin, Tehran, Iran to assess 439 mothers’ dental health status from pregnancy up to 2 years after delivery in intervention (n = 239) and control groups (n = 200). Mothers in intervention groups received oral health-related education consisted of nutritional and behavioral messages via either of four methods: A: comprehensive method including all following methods together (n = 74), B: group discussion by dentists (n = 59), C: face to face education by primary health care providers (n = 53), and D: social network applications (n = 53); while those in control group only received routine maternal and oral health care. We used a questionnaire to collect mothers’ demographic, socioeconomic and dental care behavior data and also performed oral examinations to assess their DMFT at baseline and 24 months after delivery to evaluate the effectiveness of these educational oral health interventions.ResultsFrom 454 mothers participated the examination session, 18 pregnant women discontinued during the follow-ups and 439 were remained with mean age of 27.47. In the intervention group, the frequency of daily brushing among women increased from 64% at baseline to 85.6% at the last follow-up and the mean D significantly decreased nearly 1unit at same period (P < 0.05). Most and least dental caries changes were in comprehensive intervention group and social network intervention group compared to other intervention groups, respectively.ConclusionsPerforming educational interventions during and after pregnancy using various message delivery methods and messengers (oral health professionals and trained PHCPs), could improve oral health status and behaviors of pregnant and lactating mothers in a feasible and applicable manner.

Alt-tech social forensics: Forensic analysis of alternative social networking applications

Mainstream social platforms boast billions of users worldwide. In recent years, popular social platforms have seen a decline in their users that are choosing to migrate to alternative-tech social applications reinforced by frustrations of mainstream social platforms over alleged censorship of free speech and banning of predominant public figures such as the former president of the United States (U.S.). As such, group effect of similar minded users on alternative-tech social platforms may lead to fostering events such as the U.S. Capitol attack on January 6th, 2021, where the spreading of false information and extremist ideologies through alt-tech applications such as Parler and MeWe took place. These cases demonstrate the immense forensic need to understand how alternative-tech social applications operate and what they store about their users' personal information and activities. We present the primary account for the digital forensic study of (n = 9) alternative-tech social applications used on Android and iOS devices. Our analysis includes Parler, MeWe, CloutHub, Wimkin, Minds (Minds Mobile and Minds Chat), SafeChat, 2nd1st, and GETTR. Results revealed that some applications do store unencrypted user information on the devices, such as usernames, phone numbers, email addresses, posts and comments, and private chat messages. Furthermore, some security vulnerabilities were discovered that allow users to download data that should have been private (such as sent private images) without authentication and authorization by other users. Finally, to aid in the analysis and automatic extraction of relevant evidence, we share Alternative Social Networking Applications Analysis Tool (ASNAAT), that automatically aggregates forensically relevant data from the alt-tech social networking applications when presented with a mobile device's forensic image.

소셜네트워크 앱 중독의 선행변수와 결과변수

The purpose of this study is to explore whether the fear of missing out and psychological enhancement really affect social network application addiction, and how social network application addiction leads to users’ life satisfaction and continuous usage intention. In addition, this paper will also focus on whether the poisoning of social network application addiction plays an intermediary role between the use motivation and psychological behavior results. The data were collected by questionnaire, and the social network users aged 10-30 in China were investigated. Finally, 649 valid questionnaires were analyzed. The analysis results of this study are summarized in the following four aspects: Firstly, this study reveals the positive influence of fear of missing out and psychological enhancement on social network application addiction. Secondly, this study confirms that social network application addiction has a negative impact on life satisfaction and a positive impact on users’ continuous usage intention. Thirdly, the analysis of the mediation effect of social network application addiction shows that the fear of missing out leads to life satisfaction and continuous usage intention through social network application poisoning. At the same time, this study draws a conclusion that psychological enhancement through social network application addiction leads to life satisfaction and continuous usage intention.

A New Approach for Social Networks Based on Ontology of Multilingual Dynamic Groups

The social groups in social network applications in their current form have major problems related to structural inertia, group growth period, multilingualism, research and identification, group’s privacy settings, and authority of the Admin. This work uses the concept of ontology as a solution to these problems through an approach that makes the automatic creation and growth of social groups, the democratization of social groups by separating the concept of structure and the concept of views. In addition, this approach opens up a new type of social group «multilingual dynamics groups»; using the conceptual indexing of social groups to distinguish between the definition of the concept (Interest) and the definition of the term (multi-lingual). This work constitutes an important addition and contribution for renewing the concept of social groups through automatic growth, democracy (Self-management), dynamic (generalization, specialization, union, intersect and minus), semantic relationships, multiple languages, mono name ontological concept.

SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs.

Graph data structures have been used in a wide range of applications including scientific and social network applications. Engineers and scientists analyze graph data to discover knowledge and insights by using various graph algorithms. A breadth-first search (BFS) is one of the fundamental building blocks of complex graph algorithms and its implementation is included in graph libraries for large-scale graph processing. In this paper, we propose a novel direction selection method, SURF (Selecting directions Upon Recent workload of Frontiers) to enhance the performance of BFS on GPU. A direction optimization that selects the proper traversal direction of a BFS execution between the push and pull phases is crucial to the performance as well as for efficient handling of the varying workloads of the frontiers. However, existing works select the direction using condition statements based on predefined thresholds without considering the changing workload state. To solve this drawback, we define several metrics that describe the state of the workload and analyze their impact on the BFS performance. To show that SURF selects the appropriate direction, we implement the direction selection method with a deep neural network model that adopts those metrics as the input features. Experimental results indicate that SURF achieves a higher direction prediction accuracy and reduced execution time in comparison with existing state-of-the-art methods that support a direction-optimizing BFS. SURF yields up to a and speedup over the state-of-the-art graph processing frameworks Gunrock and Enterprise, respectively.

Self-Image Construction in New Media through the Instagram Application (Semiotic Analysis of @maudyayunda Instagram posts)

New media present as communication that individuals choose for social interaction in cyberspace. One of them is Instagram, which is one of the uses of new media in the form of a social media network application that functions as a place to share photos and videos with other users. Instagram makes it easy to form a person's self-image, both positive and negative images in any angle. Like Maudy Ayunda, a public figure who has a good self-image on her Instagram account. This study aims to find out how self-image can be built through Instagram account posts. This type of research is descriptive research with a qualitative approach method that uses a constructivist paradigm with Ferdinand de Saussure's semiotic theory. The results of this study are self-image on the @maudyayunda Instagram account formed from components of verbal and non-verbal messages which can be seen from the description of photo uploads with dress styles and photo styles at certain moments. So from this study it can be concluded that Maudy Ayunda has a personality in building her image that is passionate about learning, intelligent, inspiring, and has a healthy lifestyle.

Social network analysis and applications: A review of the broad research aspects of social network structure

The use of social media has generated huge Online Social Networks (OSN). This network exhibits nice structural properties and has opened door to various research tasks like community detection, link prediction and influence modeling in OSN. There are several models such as linear threshold and independent cascade model proposed in the literature that models the influence diffusion process. Depending on the use-case one may want to amplify or attenuate information in OSN called influence maximization (IM) and rumor blocking (RB), respectively. In terms of marketing, it is called viral marketing. This paper extensively reviews the fundamental research tasks associated with the structure of OSN, groups them into different categories based on the fundamental research area discussed along with its applications. This paper also surveys RB and the various variants of IM found in the literature.

Teaching presence in students’ WhatsApp groups: Affordances for language learning

With the recent COVID-19 pandemic and disruption of campus-based education, the use of mobile social networking applications to supplement formal education has attracted a great deal of attention. Teachers do have opportunities to join students’ online groups to share, clarify, and exchange housekeeping information and course-related content with them. Teachers can, in particular, provide English as a foreign language (EFL) students with more sources of linguistic input, interaction, and feedback. Research investigating this potential, however, is still scarce in such contexts. The current study explores the likely affordances of teaching presence in students’ WhatsApp groups for designing, facilitating, and guiding cognitive and social processes conducive to their language learning. A mixed-method design was employed to collect both quantitative and qualitative data and information from English-major undergraduates (N = 111) and faculty teachers (N = 8) who joined the same WhatsApp groups for one academic semester at a major university in Oman. Descriptive and thematic analyses of data from a survey with both closed-ended and open-ended questions and semi-structured interviews indicate that the shared WhatsApp groups functioned as small close-knit communities where students were able to constantly access teachers for their assistance, feedback, and clarification of content. Despite these merits, however, the participating faculty believed that the presence of teachers in WhatsApp groups might have consequences for students’ tolerance of ambiguity, scaffolding, and autonomous language learning. The paper concludes by discussing several pedagogical implications and directions for future research.

Design as sexual practice: The visual culture of social apps and HIV risk in Taiwan

This article explores the visual culture of social network applications (apps), using “safer-sex design” as an anchor to contemplate how different practices of looking at HIV co-constitute viral visibility. Drawing on science and technology studies and queer theories, this article traces viral visibility from its digital production through its marketization, and finally to its implications for gay men’s sexual communication in Taiwan. Through interviews with gay Taiwanese men and a social app developer and the visual analysis of Hornet, Grindr, and Scruff, this article describes how the precarity of viruses, knowledge of HIV prevention, and social stigmas against sexual minorities are brought together, staged, and made eligible (and ineligible) for public viewing. This article demonstrates how digital platform designs work to facilitate gay men’s sociosexual communication while ironically reinforcing stigmas against HIV/AIDS in Taiwan, and suggests a critical approach to the visual culture of social apps and queer health.

Decreasing the Screen Time on Social Media using Time Limitations

Overgrown technology spoils audiences with current features, such as social networking, which is a component of today's technical advancements. In recent years, social networking applications have had a significant impact on individuals' health and most of them are students. Many platforms include elements that encourage people to share their impressive routines on these networks. One of the issues that arise as a result of abuse and inadequate use is the rising rate of depression. In addition, Excessive screen time has become a major issue as digital technology has advanced. As a result, researchers and practitioners have turned their attention to digital well-being. The purpose of this study is to reduce the screen time for we proposed the possible approach.

Validity and Reliability of the Persian Version of COVID-19 Anxiety Syndrome Scale Among the Iranian General Population.

The crisis of the COVID-19 prevalence in Iran, as well as the world, caused mental disorders and anxiety syndrome. The COVID-19 anxiety syndrome scale (C-19ASS) assesses conceptually and psychometrically the nature of the COVID-19 threat experience instead of a response to the threat, fear, and COVID-19 anxiety. Therefore, the aim of this study is to evaluate the psychometric properties of the Persian version of the anxiety syndrome scale of COVID-19 in the population of Iran. The Persian version of C-19ASS was sent to Iranian adults via online social networking applications and finally, 932 adults responded to the questionnaire. The results of exploratory factor analysis revealed two-factor structures for C-19ASS, which explained 48.70% of the total variance. Given the confirmatory factor analysis findings, all goodness of fit indices confirmed the model fit. All coefficients of internal consistency were estimated as acceptable reliability. The results showed that the C-19ASS has good psychometric properties, and can be used by researchers, psychologists, and healthcare providers to assess the anxiety syndrome of the Iranian population during the COVID-19 pandemic.

Increasing legal awareness for national defense through socialization on social networking applications in Indonesia

The primary purpose of this study is to increase legal awareness of national defense efforts through socializing activities on social network media in Indonesia. Our data is obtained from various publication sources in books, magazines, articles, and other websites. Furthermore, we analyzed the data using a phenomenological approach to get many conclusions to get valid findings to answer the problem. Based on the analysis and discussion of the data, we can report that efforts to increase legal awareness for the Indonesian generation to increase national resilience through activities on social media are by providing awareness, understanding, and transparent legal sanctions on how activity on social media is not against the law either individuals or groups and any parties so that later it reflects a community of young Indonesians who are aware of the law and eventually become a national power. We hope that these findings will serve as a reference for future studies.

Integrated data management: New perspectives for management control

The purpose of this special issue is to contribute to the international debate on novel approaches to corporate data management for the improvement of man-agement control systems. The theme embraces the interdisciplinary relationships that management controls and the accounting function that it, increasingly, should have with other disciplines. These relationships are permeated by both quantitative and qualitative approaches. Quantitative models include those created through data science and those that refer to mathematics, statistics, and information tech-nology. Moreover, corporate data management models combining qualitative and quantitative approaches are explored and discussed within disciplinary areas that, due to their consolidated history, are close to management control: e.g., legal, soci-ological, historical and other humanities areas. This theme embraces not only business data management but also knowledge management; it is not only about internal (accounting and non-accounting) data, but also about external data of a statistical, economic, and social nature, which are of interest from different disci-plinary perspectives concerning the integrated management of accounting data and Big Data. With the development of new technologies, such as the ‘Internet of Things', and the increasingly extensive applications of blockchain, social net-works, and mobile devices, organizations are generating huge volumes of data in different formats much faster than in the past. In this sense, big data analytics techniques present opportunities to improve decision-making processes of both a strategic and an operational nature, due to their ability to extract knowledge from data, to facilitate problem solving, and to favor predictive and prescriptive ap-proaches to business phenomena. From an organizational point of view, it is im-portant to analyze the impact of Big Data on the professional profiles of actors typically involved in accounting and management control processes.

Sentimental Analysis on Live Twitter Data

Abstract: The entire world is transforming quickly under the present innovations. The Internet has become a basic requirement for everybody with the Web being utilized in every field. With the rapid increase in social network applications, people are using these platforms to voice them their opinions with regard to daily issues. Gathering and analyzing peoples’ reactions toward buying a product, public services, and so on are vital. Sentiment analysis (or opinion mining) is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects. In recent years, researchers in the field of sentiment analysis have been concerned with analyzing opinions on different topics such as movies, commercial products, and daily societal issues. Twitter is an enormously popular microblog on which clients may voice their opinions. Opinion investigation of Twitter data is a field that has been given much attention over the last decade and involves dissecting “tweets” (comments) and the content of these expressions. As such, this paper explores the various sentiment analysis applied to Twitter data and their outcomes.

Long- and Short-Term Preference Modeling Based on Multi-Level Attention for Next POI Recommendation

The next point-of-interest (POI) recommendation is one of the most essential applications in location-based social networks (LBSNs). Its main goal is to research the sequential patterns of user check-in activities and then predict a user’s next destination. However, most previous studies have failed to make full use of spatio-temporal information to analyze user check-in periodic regularity, and some studies omit the user’s transition preference for the category at the POI semantic level. These are important for analyzing the user’s preference for check-in behavior. Long- and short-term preference modeling based on multi-level attention (LSMA) is put forward to solve the above problem and enhance the accuracy of the next POI recommendation. This can capture the user’s long-term and short-term preferences separately, and consider the multi-faceted utilization of spatio-temporal information. In particular, it can analyze the periodic hobbies contained in the user’s check-in. Moreover, a multi-level attention mechanism is designed to study the multi-factor dynamic representation of user check-in behavior and non-linear dependence between user check-ins, which can multi-angle and comprehensively explore a user’s check-in interest. We also study the user’s category transition preference at a coarse-grained semantic level to help construct the user’s long-term and short-term preferences. Finally, experiments were carried out on two real-world datasets; the findings showed that LSMA modeling outperformed state-of-the-art recommendation systems.

Multi-armed bandit based distributed resilient consensus and its applications in social networks

This paper addresses the resilient consensus problem in the presence of faulty nodes whose state updating is randomly unreliable. Unlike the existing approaches using elimination of extreme neighbor nodes with state updating or trustworthiness evaluation of neighbor nodes by historical information, this paper presents a novel multi-armed bandit based algorithm. The idea is to increase the selection probability of the so-called healthy running subsets against that of the unhealthy running subsets, according to the evaluation of the reward and credibility functions. As a result, the normal nodes in a network can achieve consensus with the influence from the faulty nodes mitigated. The algorithm can also be applied in a social network with antagonistic weights.