Mobile Social Networks Research Articles

Multiple Cooperative Task Assignment on Reliability-Oriented Social Crowdsourcing

With the rapid development of mobile devices, mobile social networks have drawn increasing attention from spatial crowdsourcing in which users sharing information via social networking applications can easily identify and participate in multiple cooperative tasks. Existing studies generally assume that all users are trustworthy and can reliably perform assigned tasks. However, such assumptions do not hold in real-world practices. In this article, we consider an essential crowdsourcing problem, namely Reliability-oriented Socially-Aware Crowdsourcing (R-SAC), which improves the reliability by recruiting users who are better matched to the tasks. Our R-SAC problem is to recruit reliable users for multiple cooperative tasks so that the overall reliability of task assignment is maximized. We prove that the R-SAC problem is <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hard. Then, we propose an approximation algorithm with a factor of <inline-formula><tex-math notation="LaTeX">$\ln {m} + 1$</tex-math></inline-formula> to solve the R-SAC problem, where <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> is the number of tasks. Specifically, user reliability refers to the probability that a user can reliably perform assigned tasks. To achieve reliable user recruitment during task assignment, we formulate the reliability of a user by combining the matching between the user and tasks, and the reliability feedback from neighbors who share similar behaviors with the user in the social network. Besides, the distributed collaborative filtering technique is utilized to select the reliability feedback from the neighbors. We evaluate the performance of our proposed approach experimentally on two widely-used real-world datasets and the results demonstrate that our approach significantly outperforms five representative approaches.

Multiple Cooperative Task Allocation in Group-Oriented Social Mobile Crowdsensing

Mobile crowdsensing, a new paradigm, has drawn much attention from the online community, in which mobile users are connected by using smartphones with sharing of information via mobile social networks. Multiple cooperative task allocation (MCTA) is a crucial problem in mobile crowdsensing, where each task requires more than one user to cooperatively complete. As more and more users join sensing tasks in groups, it is indispensable to develop a group-oriented crowdsensing mechanism supporting MCTA. However, existing studies generally focus on a group that can provide sufficient users to accomplish a task. Once these groups no longer exist, the corresponding task will be discarded or be performed with compromised quality. In this article, we propose a novel three-phase approach named Group-oriented Cooperative Crowdsensing (GoCC) to tackle the MCTA problem in social mobile crowdsensing. This approach exploits real-life relationships in the social network to form compatible groups, which improves the task coverage via group-oriented cooperation while achieving good task cooperation quality. Specifically, phase 1 selects a subset of users on the social network as initial leaders and directly pushes sensing tasks to them. Phase 2 utilizes the leaders to search for their socially connected users to model groups. Phase 3 presents the process of group-oriented task allocation for solving the MCTA problem. Experiments on the real-world dataset validate that our approach significantly outperforms the representative approaches.

Omnichannel Shopping Experience: An Exploratory Study on a Generation Z Sample

The increasing adoption of new technologies, such as mobile and smart equipment and social networks, and the increasing deployment of technological solutions in stores create new opportunities and challenges for retailers. As the separation between online and physical channels faints, the omni-channel approach is gaining ground, seeking to deliver a seamless customer experience regardless of the channel. Considering the integration of channels, the impact of mobile technologies, the importance of social media, the changing role of physical stores and the need to respond to new consumer requirements, this article explores the omnichannel phenomenon with a focus in the new generation of digital natives, Generation Z, suggesting questions for further research and opportunities for formulating marketing strategies suited to ongoing developments.

The Use of Digital Media and Modes of Communication of Affected People: A Case Study of Earthquakes in East Java, Indonesia

Several earthquake incidents in the East Java province have resulted in numerous disadvantages such as damaged buildings and victims that were physically and emotionally wounded. The problem that has arisen in these situations is how victims of natural disasters often receive late responses and little to no information about relief efforts from authorities. Local governments and disaster relief organizations have not initiated the right method of communication to give important information toward disaster victims when information technologies have developed rapidly. The present study opt for a qualitative research design to conduct the interviews from the victims of disaster living nearby Malang and Blitar regencies of Indonesia. It examines how people living in earthquake-prone regions are communicating during the disaster using smartphones and social networking services. The findings of the research show that people living in the disaster-risk areas use mobile phones, smartphones, and social networking services in their daily life; yet, not many of them using these devices and services to look for information about earthquakes. Although authorities have created digital channels as a source of information, it does not receive significant attention and interest of the people at risk. The problems with uneven digital media literacy and lack of resources remain unresolved. Cooperation between authorities and people is expected, to ensure that the use of existing communication technologies becomes more effective and efficient in earthquake relief efforts and countermeasures.

An investigation of user’s continuance intention towards Mobile Social Network Sites (SNSs)

In recent years, the popularity of Smartphone social networking sites has exploded, however, in prior studies the link between users' values, satisfaction, and continued desire to use SNSs was viewed as having little attention. The objective of this research is to establish a model that explores users' value and satisfaction as key antecedents of continuance intention towards the usage of mobile SNSs. Data was collected through a paper-based survey from a university and 532 valid responses from students were used for analysis. To validate our proposed model, and test the hypothesis, the Partial least square technique has been applied through smart pls. The empirical results revealed that both Hedonic value and Utilitarian value were significant antecedents of intention to continuance. Moreover, satisfaction was also found important determinant of intention to continue. The findings of this study will be helpful for marketers, web designers, and researchers to understand the importance of users' value and satisfaction in the context of mobile SNSs. Research implications are presented and directions for future studies are also suggested at the end of the study.

A Trusted and Privacy-Preserved Dispersed Computing Scheme for the Internet of Mobile Things

Existing computing paradigms of the Internet of Mobile Things (IoMT) and social networks cannot effectively serve users due to their limited computing ability, dynamic mobile networks, weak connectivity, and high energy consumption and investment costs. Dispersed computing (DCOMP) is a promising way to solve the above issues. However, DCOMP is emergent, and few studies apply DCOMP to IoMT and social networks. Moreover, security problems also arise when introducing DCOMP to IoMT and social networks. In this paper, we propose a novel reference architecture to realize DCOMP in IoMT or social networks. We also propose two models—a trusted application discovery and acquisition model and a security domain-based computing offloading model—to enhance the security of the proposed architecture. The key idea of the first model is to use blockchain to construct a trusted application storage and acquisition system. This system guarantees that the tasks offloaded to dispersed devices are trusted. In the second model, we design two algorithms to offload tasks to appropriate dispersed devices to protect users’ privacy as much as possible. The experimental results prove the effectiveness of the proposed scheme.

A Study of Occupational Therapy Strategies and Psychological Regulation of Students' Internet Addiction in the Mobile Social Media Environment.

This paper provides an in-depth research analysis of occupational therapy strategies and psychological regulation of students' Internet addiction in the existing mobile social media environment. Based on the definition of Internet use peer pressure, a preliminary scale with 2 dimensions of specific behavioral pressure and psychological cognitive pressure was developed. First, a professor of mental health education and a master's degree student in mental health education were invited to rate the scale, while 47 junior high school students were selected to try the scale to ensure that the questions of the scale were clearly expressed. Secondly, 461 subjects were selected to take the initial test of the scale, and item tests and exploratory factor analysis were conducted to ensure that the structure of the scale was as expected and the scale was expressed to psychometric standards. Finally, 810 subjects were selected for the scale retest, and item analysis and validated factor analysis were conducted, and their results met the psychometric criteria. Student peer attachment significantly and positively predicted adolescent Internet addiction; Internet use peer pressure played an incomplete mediating role in the prediction of peer attachment to Internet addiction; loneliness played a moderating role in the pathway of peer attachment to Internet addiction, and loneliness also played a moderating role in the pathway of peer attachment to Internet use peer pressure. The attitude towards online games is entertainment to kill time, a tool to escape the pressure of study and to gain the fun of friends comparing with each other. The main online motivations of adolescents included individual and situational factors, and behaviors were characterized by pan-entertainment and gamification, mainly through mobile social networks to obtain external information and knowledge, maintain interpersonal relationships, and gain a sense of belonging; they were characterized by a fixation on time, space, and online content. The interaction of family members is more of a behavioral habit, and the communication habits and behavior patterns established in the family will influence the construction of children's relationships with others, while parents' head-down behavior will also influence children's attention to various needs of an Internet-dependent behavior problem, which in turn affects children's problem-solving and intimate relationships with family members.

University life has gone digital: influences of institutional mobile social network use during the COVID-19 emergency

PurposeMany universities implemented institutional social networking apps as an alternative to in-person social experiences during the COVID-19 pandemic. Therefore, this study aims to explore previously identified factors that influenced intentions to form collective actions, also known as we-intentions, on such social networking apps and their influence on student satisfaction with the app artifact.Design/methodology/approachStudents from across a large university were invited to participate in a survey. Responses from 915 students who reported using the app were analyzed using a maximum likelihood covariance-based structural equation model. Analysis was conducted using the R programming language's psych, lavaan, and semTools packages.FindingsThe authors found that we-intentions are positively associated with recent app use and with student satisfaction with the app. Group norms were found to significantly influence the formation of we-intentions, while social identity is positively associated with both we-intentions and satisfaction.Originality/valueThe paper provides evidence that past research generalizes to the context of university mobile social networks and identifies a relationship between we-intentions and satisfaction in this context. It also provides practical insight into factors that influence we-intentions, and subsequently students' online education experience, in the context of a university's institutional mobile social network.

Dual-Mode Data Forwarding Scheme Based on Interest Tags for Fog Computing-Based SIoVs

Social Internet of vehicles (SIoVs) is a combination of vehicular ad-hoc networks (VANETs) and mobile social networks (MSNs). Although social relationships between vehicle nodes are more stable than location changes of vehicle nodes in SIoVs, there always are the problems of data forwarding optimization and adaptability for dynamic networks. In this paper, we propose a dual-mode data forwarding scheme based on interest tags for fog computing-based SIoVs. In the first data forwarding mode, the vehicle nodes calculate and use the cooperation degrees to select the next cooperative forwarding nodes until the data is forwarded to the destination node. In the second data forwarding mode, the RSUs assist the data forwarding of vehicle nodes based on the RSU ranking mechanism of interests, where the fog servers calculate the RSU ranking table of all the interests and the related RSU selects the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> RSUs to forward the data according to the RSU ranking table. The experimental results show that our proposed scheme is more efficient and stable than other related schemes by the comparisons of delivery ratio, overhead ratio and average hop count.

Uncovering the Heterogeneity in Fitness App Use: A Latent Class Analysis of Chinese Users.

This study examines fitness app use patterns and their correlates among Chinese users from the perspectives of uses and gratification theory and self-determination theory. Our sample comprised 632 users of WeRun, the fitness plugin of WeChat, the largest Chinese mobile social networking app; participants completed an online survey and provided self-tracked physical activity data, which were subjected to latent class analysis. Based on the four-class latent class model (which yielded the best model fit and the most interpretable results), 30.5%, 27.5%, 24.7%, and 17.3% of the users were categorized as light users, reward-oriented users, lifestyle-oriented users, and interaction-oriented users, respectively. Moreover, class membership was associated with gender, age, education, income, life satisfaction, autonomy, and platform-based motivations. There is a significant heterogeneity in fitness app use and exercise behaviors. Platform-based motivations and autonomy are important classification factors, as users are looking for specific kinds of gratification from their use of fitness apps. Demographics and individual characteristics are also explanatory factors for class membership. The study findings suggest that fitness app designers should segment users based on motivation and gratification.

IMPROVING STUDENT’S SPEAKING ABILITY THROUGH MOBILE SOCIAL NETWORKING (Case Study : First Year Students of Stikes Yarsi Mataram

The aimed of this research was to find out whether the implanting of mobile social networking can be able to improve the student’s ability in speaking term of accuracy and fluency at first year students’ of Sikes Yarsi Mataram. This research was conducted through pre-experimental method. The population of this research consisted of one class of first year students’ of Sikes Yarsi Mataram which consisted of 31 students at Sikes Yarsi Mataram academic year 2020/2021. In collecting data, the researcher gave a speaking test, the speaking test help into two steps, there were pre-test and post-test, while to analyzed data , the researcher used (t)test. The findings of this research showed that the mean score in pre-test in term of pronunciation was (3,06) and post-test was (5,65). The mean score in pretest in term smoothness was ( 3,39) and post-test was (5,74). And the total result of pronunciation and smoothness or X1 was (3,22) and the post-test or X2 was (5,7). So, this is showed students got improvement in their speaking after being taught through mobile social networking, the students ability in speaking English became better than before

A Study of Sentiment Analysis Algorithms for Agricultural Product Reviews Based on Improved BERT Model

With the rise of mobile social networks, an increasing number of consumers are shopping through Internet platforms. The information asymmetry between consumers and producers has caused producers to misjudge the positioning of agricultural products in the market and damaged the interests of consumers. This imbalance between supply and demand is detrimental to the development of the agricultural market. Sentiment tendency analysis of after-sale reviews of agricultural products on the Internet could effectively help consumers evaluate the quality of agricultural products and help enterprises optimize and upgrade their products. Targeting problems such as non-standard expressions and sparse features in agricultural product reviews, this paper proposes a sentiment analysis algorithm based on an improved Bidirectional Encoder Representations from Transformers (BERT) model with symmetrical structure to obtain sentence-level feature vectors of agricultural product evaluations containing complete semantic information. Specifically, we propose a recognition method based on speech rules to identify the emotional tendencies of consumers when evaluating agricultural products and extract consumer demand for agricultural product attributes from online reviews. Our results showed that the F1 value of the trained model reached 89.86% on the test set, which is an increase of 7.05 compared with that of the original BERT model. The agricultural evaluation classification algorithm proposed in this paper could efficiently determine the emotion expressed by the text, which helps to further analyze network evaluation data, extract effective information, and realize the visualization of emotion.

Anger control is the most influential risk factor of mobile phone addiction among nursing and midwifery students of Zahedan University of Medical Sciences

BackgroundReligious attitude and anger management are two psychopathological constructs receiving little empirical scrutiny in relation to smart phone addiction, but theoretically should demonstrate significant relationships. Today one of these new media that is used by many people around the world, is the mobile phone. Students are one of the most important groups that are affected by mobile social networks. The aim of this study was to determine religious attitudes and anger management as risk factors of mobile phone addiction in nursing and midwifery students. MethodsThis study was a cross-sectional study involving 200 nursing and midwifery students. Relevant data were collected through demographic information questionnaire, anger management skills questionnaire, religious attitude questionnaire, and mobile phone addiction questionnaire. A multiple regression model was used to examine the relationship between variables. ResultsThe mean age of the research subjects was 22.04 ± 3.30 years. Anger control and religious attitude were a significant risk factors of smartphone addiction (β=-0.500; p=0.000; β= -0.069; p=0.004, respectively). The variables of anger ýcontrol and spiritual attitude can predict and explain 33.6% and 2.7% (36.3% in total) of the ýchanges in the mobile addiction score. Anger control is the most influential risk factor of mobile phone addiction among nursing and midwifery students (Beta = -0.385). ConclusionFindings indicate the importance of controlling anger and strengthening religious attitude in reducing the rate of mobile phone addiction in students. This provides guidance to the future development of smartphone addiction prevention programs for students.

College Student Social Dynamic Analysis and Educational Mechanism Using Big Data Technology.

With the advancement of the “big data” technology, college students inadvertently purchase personal advice while taking advantage of the exciting Internet to access information quickly and easily. In order to objectively achieve the real office of college students' material enlightenment penetration in the mobile-friendly network, we choose the popular mobile social network, and we apply the natural clustering algorithm rules to segment the college students. Further, we identify college students, based on which we construct information leakage and apply the risk assessment design. The comprehensive entrepreneurial evaluation of the microblog platform combined with the user's mobile complaints is utilized to conduct a psychological analysis on the key components and key communication channels of college students' complaint leakage. We obtain ticket data using the social prospect method and refer to four dogmatic characteristic elements of query motivation. And we also collect dimensions through surrogate analysis. Based on the reference feature factor, four different user groups are rapidly moved. Dining, social, teaching and large users, and the model features of various usage profiles are described in combination with eight categories of user feature importance. The |objective is to improve college students' awareness of social network and mobile partner network mobile information to a certain extent. We attempt to protect college students from fraud and installation plans, to standardize the management of online social platform of advertisements, and to progressively promote the disposal of network movable property due to security changes.

Deep Information Fusion-Driven POI Scheduling for Mobile Social Networks

With the growing importance of green wireless communications, point-of-interest (POI) scheduling in the mobile social network (MSN) environment has become important in addressing the high demand for innovative scheduling solutions. To enhance feature expressions for the complicated structures in MSNs, this article explores a deep information, fusion-based POI scheduling system of the MSN environment via the implementation of an edge-cloud deep hybrid sensing (PS-MSN) framework. Cloud sensing modules utilize the explicit contextual real-time information for each user, while edge sensing modules detect the real-time implicit linkages among users. Based on these two types of modules, a deep representation scheme is embedded into the hybrid sensing framework to improve its feature expression abilities. As a result, this type of framework is able to integrate multisource information so that more fine-grained feature spaces are built. In this work, two groups of experiments are conducted on a real-world dataset to evaluate the efficiency, as well as stability, of the designed PS-MSN. Using three benchmark methods to make comparisons, the excellent overall performance of PS-MSN is properly verified.

Influence Maximization Based on Network Motifs in Mobile Social Networks

A mobile social network (MSN) is a mobile communications system that involves the social relationship of the users, in such a network, mobile users can spread information, opinions, ideas, rumors. Influence Maximization (IM) aims to identify <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nodes from a network such that the influence spread generated by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nodes is maximized, which has been attracting increasing attention in recent years. However, existing methods of influence maximization are heuristic algorithms based on network topology and greedy algorithms based on spreading. Accordingly, in this paper, we focused on Network Motifs (NM) as drivers of influence to impact the spreading process, we proposed IM-NM, a network motifs-based influence maximation scheme for delivering information efficiently. In consideration of the communication relationship and the users’ attributes, we first defined Weight Ratio (WR), Degree Density (DD), and Structural Stability Level (SSL). Then we identified the key network motifs by Naive Bayesian machine learning. Finally, we adopted the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> key network motifs as the unit structure to reconstruct the network, and select the bridge node with strong communication ability in the key motifs to maximize the information. We implement our proposed methods on a set of real-world networks to evaluate the performance, the experimental results demonstrate that our proposal achieves better performance than other related methods.

The Effect of Using Whatsapp on EFL Students' Medical English Vocabulary Learning During the Covid-19 Pandemic

The role of social networking mobile applications such as WhatsApp in enhancing second language vocabulary learning among English language learners continues to be a subject of interest for many scholars. The current study aimed at examining medical English vocabulary learning among undergraduate students using WhatsApp compared to learning vocabulary via Blackboard platform during the Covid-19 pandemic. To this end, 108 medical students (51 males, 57 females) enrolled in a first semester English for a specific English course participated in the study. A quasi-experimental design was adopted for two groups. Fifty-three students were assigned to the WhatsApp group and 55 students were assigned to the Blackboard group. Data were collected using pretest-posttest design. Results of t-test scores revealed no significant difference between the WhatsApp and Blackboard groups on a vocabulary test. Results of survey that gauged students' perception of the use of WhatsApp as a platform for learning new vocabulary showed positive perceptions since participants thought that WhatsApp enhanced their learning experience.

Religion and the Internet: Prophetic Medicine in the Age of Modern Technology

This paper discusses the distribution of the Prophet's medicine using mobile applications and social networks “Vkontakte” and “Telegram”. When you enter the words “medicine of the Prophet”, “sihr”, “evil eye”, “witchcraft”, “exorcism of genies”, etc., into the social network search bar, more than a hundred open groups come out, which can be divided into several categories: groups providing hij ama services; groups that sell medicines and oils allowed by Islam; groups in which various herbal remedies (black cumin, garlic, ginger, etc.) are recommended for improving health and prevention; groups dedicated to ruqya. Special attention was paid to the community “Treatment and expulsion of genies” in VKontakte. The community is interesting by fact that you can fi nd specialists in expelling genies, as well as you can off er the services relevant to the group by yourself. The search bar in the Play Market gives out an impressive amount of applications dedicated to the medicine of the Prophet. The author of the article focuses on the application “Anti Sihr: a cure for witchcraft”, where the users are invited to use three tests to independently determine whether a person is under the infl uence of the evil eye or witchcraft, or whether he is a subject to obsession.

A New Contemporary Profession as Game Boosters: The Behavioural Emergence of Intellectual Opportunism

This study investigates the emergence of intellectual opportunism for game boosters in mobile social networks. It shows the supply and demand for upgrading game levels in mobile social networks. These supply and lucrative demand highlight a new temporary profession as game boosters. Game boosters facilitate framed newbie gamers who want to play at a higher game level without playing for a long time. This emergence of professionally game boosters is due to economic opportunism, promising their potential future incomes. Moreover, opportunistic mobile social networks (OMSNs) support the muscular appearance of the game booster profession. On the other hand, this study demonstrates that mobile social networks exploit hedonistic human needs by reflecting their behaviour and social relationships when building efficient and trustworthy business contracts. This study presents uniqueness supported by critical reasoning. First, it shows that the game booster phenomenon has emerged as a contemporary profession in society, promising future cash inflows above the average salaries of typical jobs. Second, this research demonstrates the sequential logic of economic opportunities, intellectual opportunism, and mobile social networks, constructing game boosters’ income generating. Nevertheless, this study acknowledges that the game booster profession has low integrity due to its members’ inconsistent and unprincipled virtues.

Improving Traffic Load Distribution Fairness in Mobile Social Networks

Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node’s sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node’s periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance.