Social Network Patterns Research Articles

Social isolation of unfamiliar cattle by groups of familiar cattle.

Domestic herbivores show a strong motivation to form associations with conspecifics and the social dynamics of any group is dependant on the individuals within the group. Thus, common farm management practices such mixing may cause social disruption. Social integration of new group members has previously been defined as a lack of aggressive interactions within the group. However, a lack of aggression among group members may not represent full integration into the social group. Here we observe the impact of disrupting groups of cattle via the introduction of an unfamiliar individual, on the social network patterns of six groups of cattle. Cattle contacts between all individuals in a group were recorded before and after the introduction of the unfamiliar individual. Pre-introduction, resident cattle showed preferential associations with specific individuals in the group. Post-introduction, resident cattle reduced the strength of their contacts (e.g., frequency) with each other relative to the pre-introduction phase. Unfamiliar individuals were socially isolated from the group throughout the trial. The observed social contact patterns suggest that new group members are socially isolated from established groups longer than previously thought, and common farm mixing practices may have negative welfare consequences on introduced individuals.

How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers

Background: This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. Methods: This study recorded the German-language Twitter stream over two months, recording about 6.7M accounts and their 75.5M interactions (33M retweets). This study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. Results: The purely structural detection approach identified an echo chamber (red community, 66K accounts) focused on a few topics with a triad of anti-Covid, right-wing populism and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community (113K accounts) was much more heterogeneous and showed “normal” communication interaction patterns. Conclusions: The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship.

The relationship between social network typology, L2 proficiency growth, and curriculum design in university study abroad

AbstractThis study utilizes social network analysis to characterize a typology of study abroad sojourner experience, detailing the relationship of social experience types to second language (L2) proficiency growth and study abroad program design. In contrast with previous research, the study performs a quantitative analysis of structural and compositional network features to identify a typology of social networks. Participants were 30 L2 Spanish learners from five US-based semester-long university study abroad programs in Spain. Social network data were collected using a social network questionnaire, while L2 development was measured through an elicited imitation task. Results identify four prominent social network patterns, characterized by varying levels of Spanish language use, emotional proximity to contacts, frequency of interaction, contact status as program peer or host community member, and network cohesion. L2 proficiency development was significantly affected by these patterns, which were shaped by the contrasting curricular models of study abroad programs.

Positive ageing in institutional homes: towards a de-stigmatization process

PurposeThis qualitative research examines the varied reasons for relocation to old age homes (OAHs) in contemporary India. The purpose of this study investigates the acceptance of institutional living in Lucknow (a Tier II city of India) and whether migration to OAHs is a voluntary decision. This study also examines the lifeworld of the older adult in these OAHs in an attempt to find out whether OAHs are conducive to positive ageing. Derivatively, the authors study their engagement/time use pattern and social networking patterns in the OAHs. Finally, the research seeks to learn whether OAHs are slowly substituting older adult care given within the family by offering the best of the facilities and services.Design/methodology/approachThis qualitative research was conducted in two private OAHs in Lucknow, India. The findings of the study are based on 28 qualitative interviews conducted with the inmates, administrative staff and caretakers. The interviews were unstructured and open-ended and were supported by observations. The observation was not only made of the social setting but also the reaction of the participants. The idea was to develop an emic view of the subject by exploring valid narratives. Pseudonyms were used to report the finding so as to maintain the confidentiality of the research subjects.FindingsThis research moves beyond the traditional wisdom that people move to OAH because of the push factors within the family. OAHs in India have evolved over the years and high-end OAHs are equipped with modern amenities to cater to the upper class in their twilight years. Residents were found to lead active lives in OAHs and their common habitus and bonding capital helped them to face the vagaries of old age more confidently. Their active life and membership in various civic organizations challenge the contention of the role theory that the aged are more prone to lose rather than gain roles.Originality/valueThe originality of the research lies in the fact that the authors are extending the arguments made by the role theory of social ageing. The theory proposes that aged people are more likely to lose out roles rather than gain new ones. This study finds that the elderly tend to live a very active life in OAHs and engaged various civic organizations. Although they may lose/voluntarily give up the roles like the head of the household, spouse, etc., they acquire new roles in the context of OAHs.

The Social Capital of Acehnese Entrepreneurs (Case Study on Fabric Traders At Bireuen Regency)

This paper aims to discuss the social capital of cloth traders in Bireuen Regency. The data collection method used in this paper is a qualitative research method through observation and interviews. In this paper, researchers use the concept of social capital to translate how the knowledge and practices of cloth traders in Bireuen are in the social networks formed by them. In addition, researchers also want to show the views, patterns of regeneration and social networks of traders. The results of the study show that cloth traders' social capital is the main foundation for entrepreneurs in sustaining and developing their business holistically.

Loneliness, Social Support Networks, Mood, and Well-Being among the Community-Dwelling Elderly, Mysore

Background: Humans are social beings. Older adults more commonly endorse social engagement than physical health when describing successful aging. Deficits in social support have been associated with a wide variety of adverse health outcomes in older age, ranging from physical health to depression and self-harm. In this study, we attempt to identify the relative contributions of subjective social isolation (loneliness) and objective social isolation, measured as support network type, to depression and well-being among the community-dwelling elderly. Therefore, the objective of the study was to estimate the prevalence of loneliness and depression and to know the relationship between social network type, loneliness, and depression among the community-dwelling elderly. Subjects and Methods: A cross-sectional study was done for a period of 6 months. Individuals aged 60 years and above were included and severely ill and bed-ridden patients were excluded from the study. Sampling method used was population proportion to size with sample size of 290. The participants were interviewed using a semistructured questionnaire, where demographic and personal data and social network pattern were collected. Depression was assessed using the Geriatric Depression Scale. Data were analyzed using Statistical Package for the Social Sciences Software, 22.0 version. Descriptive statistics like percentage, mean, and standard deviation were applied. Inferential statistical tests such as the Chi-square test were applied to find out the association and expressed it as statistically significant at a P value < 0.05. Results: Among the study participants, 44.3% were lonely, 19.4% felt hopeless, and only 38% felt satisfaction in life and happiness. "Suggestive Depression" was seen among 48.6% and "Persistent Depressive Mood" was seen in about 20.2% of participants. Loneliness explained the excess risk of depression in the widowed. Similarly, 38.2% had a nonintegrated social network and 32% had integrated social network who also reported being lonely. Conclusion: Loneliness and social networks both independently affect mood and well-being in the elderly, underlying a very significant proportion of depressed mood.

Rumor detection with self-supervised learning on texts and social graph

Rumor detection has become an emerging and active research field in recent years. At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth. However, existing works on rumor detection fall short in modeling heterogeneous information, either using one single information source only (e.g., social network, or post content) or ignoring the relations among multiple sources (e.g., fusing social and content features via simple concatenation). Therefore, they possibly have drawbacks in comprehensively understanding the rumors, and detecting them accurately. In this work, we explore contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better. Technically, we supplement the main supervised task of detection with an auxiliary self-supervised task, which enriches post representations via post self-discrimination. Specifically, given two heterogeneous views of a post (i.e., representations encoding social patterns and semantic patterns), the discrimination is done by maximizing the mutual information between different views of the same post compared to that of other posts. We devise cluster-wise and instance-wise approaches to generate the views and conduct the discrimination, considering different relations of information sources. We term this framework as self-supervised rumor detection (SRD). Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.

Analysis of Big Data and Cloud Services with Experimental of Manually and Automated Testing

Big Data has increased much focus from the scholastic world and the IT business. In the advanced and figuring world, all together is created and gathered at a rate that quickly surpasses the limit go. Right now, more than 2 billion individuals worldwide are associated with the Internet, and more than 5 billion people possess cell phones. By 2020, 50 billion gadgets are relied upon to be associated with the Internet. Now, anticipated information creation will be 44 times more prominent than that in 2009. As data is exchanged and shared at light speed on optic fiber and remote systems, the volume of information and the speed of market development increment. In any case, the quick development rate of such substantial information creates various difficulties, for example, the fast development of information, exchange speed, different information, and security. In any case, Big Data is still in its outset arrange, and the space has not been checked on all in all. Distributed computing has opened up new open doors for testing offices. New innovation and social network patterns are making an ideal tempest of chance, empowering cloud to change inside tasks, Customer connections and industry esteem chains. To guarantee high caliber of cloud applications being worked on, designer must perform testing to analyze the quality and exactness whatever they plan. In this examination paper, we address a testing natural engineering with important key advantages, to perform execution of experiments and utilized testing strategies to improve nature of cloud applications.

Factors Influencing Undergraduate Students’ Decision to Migrate to Social Network Sites as Part of their Studies

Previous research has shown that university students’ decision to use social networks for academic reasons is influenced by several factors. However, until today there is no validated instrument measuring the factors that influence undergraduates to migrate to online networks for educational purposes. The aim of this study was to propose and validate the Push–Pull–Mooring - Physical Education (PPM-PE) questionnaire and examine possible factors that contribute to students’ decisions for social network use. Participants were 302 Physical Education (PE) students from a Greek Faculty of PE and Sport Science. Data analysis with exploratory factor analysis identified a three-factor structure that measured undergraduates’ use of social networks for academic learning purposes. Multivariate analyses of covariance indicated that gender had a significant effect on students’ social networking patterns, while device access did not. Age and hours spent online significantly contributed to the above differences. Findings are discussed in relation to the PPM framework and the idiosyncrasies of online instruction within PE university contexts.

HOW THE IMPACT OF SELF REGULATION ON FEAR OF MISSING OUT (FOMO) AND INTERNET ADDICTION ON ELEMENTARY SCHOOL PRE SERVICE TEACHER STUDENTS?

The pattern of interaction and communication among young people is currently changing more towards digital communication with virtual social networks. Unfortunately, not many studies have been conducted to determine the relationship between the communication behavior patterns of social media networks and the fear of missing out. This study aims to determine whether there is an influence of student self-regulation on the variable level of internet addiction and fear of missing out on information. This research was conducted using a survey method, taking a sample of 208 active students at the University of PGRI Yogyakarta, the sample was taken using a simple random sampling technique from a population of 480 students, the data were analyzed using a simple linear regression technique. The results showed that self-regulation had no significant effect on the level of fear of missing out, but had a significant effect on the internet addiction variable. For further reasons and causes of these findings are described in the discussion.

Using network synchrony to identify drivers of social dynamics.

Social animals frequently show dynamic social network patterns, the consequences of which are felt at the individual and group level. It is often difficult, however, to identify what drivers are responsible for changes in these networks. We suggest that patterns of network synchronization across multiple social groups can be used to better understand the relative contributions of extrinsic and intrinsic drivers. When groups are socially separated, but share similar physical environments, the extent to which network measures across multiple groups covary (i.e. network synchrony) can provide an estimate of the relative roles of extrinsic and intrinsic drivers. As a case example, we use allogrooming data from three adjacent vervet monkey groups to generate dynamic social networks. We found that network strength was strongly synchronized across the three groups, pointing to shared extrinsic environmental conditions as the driver. We also found low to moderate levels of synchrony in network modularity, suggesting that intrinsic social processes may be more important in driving changes in subgroup formation in this population. We conclude that patterns of network synchronization can help guide future research in identifying the proximate mechanisms behind observed social dynamics in animal groups.

Social networks and local communication network patterns following the destructive 2018 Lombok, Indonesia, earthquake sequence

Social communication networks created during the 2018 earthquake sequence in Lombok, Indonesia using social network data from a social media platform, i.e., Facebook, and surveys in local communities were analyzed. The different modes of communication networks used during the disaster event and post-disaster mitigation were examined. Data on social media platform usage were collected from the Facebook page of the Agency for Meteorology, Climatology and Geophysics of Indonesia and the National Board for Disaster Management of Indonesia. The data show large clusters of social relation networks based on dominant issues discussed by the public under several trending hashtags. Meanwhile, the local patterns of social communication networks, particularly the flow of information and communication used by the local communities of the impacted villages in Lombok, differ depending on the culture of communication and time of accessing information. During the 2018 Lombok earthquake sequence, communication was predominantly sought from community leaders, religious leaders, family members, and neighbours. This study highlights the implementation of a decentralization system in Indonesia, which has given the local and regional authorities a significant role in disaster management, response, and emergency command for the local and regional community, instead of relying largely on the response of the central Jakarta government.

Otonomi Kolektif dan Operasionalisasi Kesadaran Publik dalam Gerakan Solidaritas Pangan Jogja (SPJ)

The Covid-19 pandemic has changed the pattern of interaction and socialization of community members. One of them is an effort to build awareness and social solidarity to help each other, especially in the dimension of domestic needs, such as food fulfillment. The Jogja Food Solidarity Movement (Solidaritas Pangan Jogja/SPJ) represents a collective action that grew because of the awareness of individuals and groups to distribute food aid to informal workers and marginalized groups in Yogyakarta. This paper examines two main points; first, how SPJ manages its social network pattern during the Covid-19 pandemic. Second, why the SPJ movement is autonomous. Using a qualitative approach and case study method, this paper finds that the SPJ movement maximizes social capital through networks and social support from non-governmental institutions, activist groups, artists, students, and the Kulon Progo Coastal Farmers Association (Paguyuban Petani Lahan Pantai/PPLP) to distribute food. to the public. The SPJ movement is formed organically, autonomously and rationally, which is a manifestation of systematically organized collective action. Through the analysis of the Resource Mobilization Theory (Teori Mobilisasi Sumber Daya/TMSD), the SPJ movement is needed to create and show collective dissatisfaction, especially during the Covid-19 pandemic, namely without which dissatisfaction is only at the individual level.

Revealing the relationship of topics popularity and bursty human activity patterns in social temporal networks

In social networks, how human activity patterns affect the popularity of topics has always been the focus of research. In this paper, a quantitative temporal analysis of the dynamics of topics popularity in Sina Weibo system was provided. Firstly, the popularity time series of 1167 topics were clustered into four clusters by K-Spectral Centroid (K-SC) clustering algorithm. Secondly, for each cluster, we calculated the exponents of topic popularity decay distribution α and the exponents of inter-activity time distribution β, respectively. Two interesting results were found: one is that the peak fraction F of topics popularity positively correlated with the topics popularity decay exponent α; the other is that bursty activity patterns in social network significantly affect topics popularity dynamics: there is a positive correlation between exponent α and exponent β. Finally, we proposed an extended SI (susceptible–infected) epidemic model with incorporate bursty human activity and verified the results by simulation.

The social network patterns and perceptions of social support in stroke survivors

Improving social networks among stroke survivors can improve their recovery and quality of life. However, there is no published baseline data regarding the patterns of social networks among stroke survivors in Singapore. This study aimed to illuminate the social network patterns of stroke survivors in Singapore and the associated satisfaction. It also examined the impact of the COVID-19 pandemic on these variables.

Social Network and Small Business Sustainability: A Case Study in Borobudur Temple Tourism Park

This study aims to determine the characteristics and patterns of the social network of souvenir traders in the Borobudur Temple Tourism Park and explore the relationship between social networks and the sustainability of the souvenir merchants’ business. In this qualitative study, researchers conducted data collection (observation and in-depth interview) between January 2018 and January 2019 before the COVID-19 outbreak hit the area. Social network data and information from the interview were analyzed and visualized with the Node-XL computer program. Our findings showed that the formation of a social network of souvenir merchants and small traders was based on kinship, friendship, interests, and contractual relationships. The structures that are maintained and developed in the Borobudur Temple Tourism Park area contain interrelated actors. While the relationship between suppliers and merchants is diagonal, the social interaction and network pattern among small traders in the Borobudur Temple Tourism Park is horizontal. Souvenir merchants and small traders in the Borobudur Temple tourism park are always maintaining their social capital to survive in their business. We can learn that each merchant is closely connected with some key fellow merchants and small or medium traders from this pattern. This network and ties among small traders can also determine trust and social capital between actors. Through each small traders’ social network, a business network based on interest, kinship, friendship, and contractual relationships can be unraveled.

Detecting communities using social network analysis in online learning environments: Systematic literature review

AbstractUncovering community structure has made a significant advancement in explaining, analyzing, and forecasting behaviors and dynamics of networks related to different fields in sociology, criminology, biology, medicine, communication, economics, and academia. Detecting and clustering communities is a powerful step toward identifying the structural properties and the behavioral patterns in social networks. Recently, online learning has been progressively adopted by a lot of educational practices which raise many questions about assessing the learners' engagement, collaboration, and behaviors in the new emerging learning communities. This systematic literature review aims to assess the use of community detection techniques in analyzing the network's structure in online learning environments. It provides a comprehensive overview of the existing research that adopted those techniques with identifying the educational objectives behind their application as well as suggesting possible future research directions. Our analysis covered 65 studies that found in the literature and applied different community discovery techniques on various types of online learning environments to analyze their users' interactions patterns. Our review revealed the potential of this field in improving educational practices and decisions and in utilizing the massive amount of data generated from interacting with those environments. Finally, we highlighted the need to include automated community discovery techniques in online learning environments to facilitate and enhance their use as well as we stressed on the urge for further advance research to uncover a lot of hidden opportunities.This article is categorized under: Algorithmic Development > Statistics Algorithmic Development > Web Mining Application Areas > Education and Learning

Analisis Jaringan Sosial Pasar Sunday Morning (Sunmor) di Universitas Gadjah Mada Yogyakarta

Sunday Morning Market is a weekly market that is open on Sundays morning in the Gadjah Mada University area. It is a place for informal traders to sell their goods. The analysis shows that there are some patterns of social networks between traders, suppliers and buyers in the Sunday Morning market. The formation of social networks among traders is a horizontal network, based onkinship ar friendship. They have the same resources and obligations to be exchanged. The social relationship itself will be manifested in help each other. The social networks formed through kinship has an important role to overcome social pressures, poverty, unemployment and limited access in getting economic resources. It is common in the informal trading sector, traditional management is applied based on friendship, kinship or origin. The formation of social networks between traders and suppliers is such a vertical social network, by which the socio-economic status is not worth, as well the exchange of obligations and resources. The relation between them is the patron-client relationship. Whereas social networks between sellers and buyers is based on the network ofinterest, social networks between sellers and buyers are based on the network of interest in each other.

Hakka migrant hometown associations: Development and social network patterns

This study sought to review the development of Hakka migrant hometown associations and to explore the social network patterns of these associations. To better understand migrant associations in different contexts, research was conducted in various countries or regions. The study examined the relationships between and among associations from an organizational perspective. Using the criteria of density and centralization, the social network patterns are classified into four network types: (1) hierarchical, (2) clique, (3) peer-to-peer, and (4) formative. According to the findings, the network characteristics of different associations enable Hakka associations to flourish in the immigration countries and preserve Hakka culture.

Using Network Structure to Identify Groups in Virtual Worlds

Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? In this paper, we address the problem of identifying social groups from conversation data and present results of an empirical study on identifying groups in a virtual world. Virtual worlds are interesting because group membership is more shaped by common interests and less influenced by cultural and socio-economic factors. Our finding is that a combination of network measures is more predictive of group membership than language cues, and that both types of features can be combined to improve prediction.