Friendship Links Research Articles

Perceptions of workplace friendships and how they have changed since the Covid-19 pandemic

Self-determination theory explains how well-being is achieved by engaging in particular behaviours. A friend in the workplace brings feelings of relatedness, autonomy and competence. Contemporary research suggests that enforced social distancing, homeworking, or hybrid working practices brought about by Covid-19, changed friendship dynamics in the workplace. A qualitative method using semi-structured interviews was conducted to explore the research question ‘Perceptions of workplace friendship and how they have changed since the Covid-19 pandemic’. Four female participants were interviewed with transcripts analysed using Thematic Analysis. Implications from the study serve to strengthen many original theories, confirming that robust research has been conducted into the area of workplace friendship. This research project went further to investigate how perceptions of workplace friendship have changed since Covid-19. Results suggest a higher level of emotional support was reciprocated between workplace friends during the pressure of working through Covid-19. Physical proximity was reported as essential in sustaining friendship links. Some friendships were difficult to continue due to social distancing or enforced separation through homeworking, the ensuing tension left many feeling uncomfortable.

Social capital mediates knowledge gaps in informing sexual and reproductive health behaviours across Africa

Advancing sexual and reproductive health is essential for promoting human rights and women’s empowerment, and combating the HIV/AIDS epidemic. A large body of literature across the social sciences emphasizes the importance of social capital, generated through the strength of social networks, for shaping health behaviours. However, large-scale measurement of social capital and social networks remains elusive, especially in the context of low-income countries. Here we delve into the role of social capital dynamics, and in particular social connectedness across communities as measured through Facebook friendship links, in shaping knowledge diffusion and behaviour related to sexual and reproductive health in 495 regions across 33 countries in Africa. Our findings demonstrate that regions with higher levels of social connectedness are more similar in their knowledge about contraception and HIV testing, as well as their adoption of these behaviours. We further observe that the influence of social connectedness becomes stronger when the knowledge gaps between regions are larger. In other words, regions are more similar in behaviours, despite knowledge gaps, when they are socially connected. These insights carry significant policy implications, especially for the design and targeting of public health campaigns. We highlight that social connectedness can serve both as a driver and an obstacle in behaviour formation, underscoring the importance of understanding its influence on health-related outcomes.

Distributional preferences in adolescent peer networks

We study distributional preferences in adolescent peer networks. Using incentivized choices between allocations for themselves and a passive agent, children are classified into efficiency-loving, inequality-loving, inequality-averse, and spiteful types. We find that pairs of students who report a friendship link are more likely to exhibit the same preference type than other students who attend the same school. The relation between types is almost completely driven by inequality-loving and spiteful types. The role of peer networks in explaining distributional preferences goes beyond network composition effects. A low rank in academic performance and a central position within the network relate positively to a higher likelihood of being classified as spiteful. Hence, social hierarchies seem to be correlated with distributional preference types.

Friendship links-based privacy-preserving algorithm against inference attacks

Directly publishing the original data of social networks may compromise personal privacy because social relationship data contain sensitive information about users. To protect the social relationships against inference attacks and achieve the trade-off between privacy and utility, we propose a privacy protection algorithm that combines the friendship links of central nodes (PPCN) in a dynamic social network. In the preparation work, we design two indices for user influence based on the characteristics of social networks that can identify central nodes (Definition 1) in a network. Operating central nodes can effectively protect user privacy and improve algorithm efficiency. Then we propose the PPCN algorithm to classify the friendship links of central nodes into three levels, which achieves the trade-off between privacy and utility. Considering that the added links may increase the risk of privacy disclosure, a substitution coefficient θ (Definition 4) is designed to measure the probability of two strangers becoming friends. Experimental results show that the privacy-utility trade-off (PUTO) value of the PPCN algorithm is an average 29.43% lower than that of other methods, achieving a better trade-off between privacy and structural utility. In addition, the PPCN algorithm only runs for 3.59 s, which performs better than most algorithms.

Social ties and home bias in mergers and acquisitions

This paper explores whether social ties, proxied by Facebook friendship links, can explain why the number and value of mergers and acquisitions (M&As) are greater within countries than between countries. We find that social ties are positively correlated with the number and value of M&As. We also demonstrate that the home bias in M&As is greatly reduced once we control for the differences in social ties between and within countries. We further find that social ties particularly facilitate M&As when the level of corruption is high, press freedom is limited in the target country, and there are more cultural differences between the acquirer and target countries.

Education Transmission and Network Formation

We propose a model of intergenerational transmission of education wherein children belong to either highly educated or low-educated families. Children choose the intensity of their social activities while parents decide how much educational effort to exert. Using data on adolescents in the United States, we structurally estimate this model and find that, on average, children's homophily acts as a complement to the educational effort of highly educated parents but as a substitute for the educational effort of low-educated parents. We also perform some counterfactual policy simulations. We find that policies that subsidize kids' socialization efforts can backfire for low-educated students because they tend to increase their interactions with other low-educated students (i.e., homophily), which reduces the education effort of their parents and, thus, their chance of becoming educated. On the contrary, policies that increase heterophily by favoring friendship links between kids from different education backgrounds can be effective in reducing the education gap between them.

Interest clustering coefficient: a new metric for directed networks like Twitter

AbstractThe clustering coefficient has been introduced to capture the social phenomena that a friend of a friend tends to be my friend. This metric has been widely studied and has shown to be of great interest to describe the characteristics of a social graph. But, the clustering coefficient is originally defined for a graph in which the links are undirected, such as friendship links (Facebook) or professional links (LinkedIn). For a graph in which links are directed from a source of information to a consumer of information, it is no more adequate. We show that former studies have missed much of the information contained in the directed part of such graphs. In this article, we introduce a new metric to measure the clustering of directed social graphs with interest links, namely the interest clustering coefficient. We compute it (exactly and using sampling methods) on a very large social graph, a Twitter snapshot with 505 million users and 23 billion links, as well as other various datasets. We additionally provide the values of the formerly introduced directed and undirected metrics, a first on such a large snapshot. We observe a higher value of the interest clustering coefficient than classic directed clustering coefficients, showing the importance of this metric. By studying the bidirectional edges of the Twitter graph, we also show that the interest clustering coefficient is more adequate to capture the interest part of the graph while classic ones are more adequate to capture the social part. We also introduce a new model able to build random networks with a high value of interest clustering coefficient. We finally discuss the interest of this new metric for link recommendation.

Leaders in juvenile crime

This paper presents a new theory of crime where leaders transmit a crime technology and act as a role model for other criminals. We show that, in equilibrium, an individual’s crime effort and criminal decisions depend on the geodesic distance to the leader in his or her network of social contacts. By using data on friendship networks among U.S. high-school students, we structurally estimate the model and find evidence supporting its predictions. In particular, by using a definition of a criminal leader that is exogenous to the network formation of friendship links, we find that the longer is the distance to the leader, the lower is the criminal activity of the delinquents and the less likely they are to become criminals. We finally perform a counterfactual experiment that reveals that a policy that removes all criminal leaders from a school can, on average, reduce criminal activity by about 20% and the individual probability of becoming a criminal by 10%.

Dynamics of social balance on networks: The emergence of multipolar societies.

Within the context of social balance theory, much attention has been paid to the attainment and stability of unipolar or bipolar societies. However, multipolar societies are commonplace in the real world, despite the fact that the mechanism of their emergence is much less explored. Here, we investigate the evolution of a society of interacting agents with friendly (positive) and enmity (negative) relations into a final stable multipolar state. Triads are assigned energy according to the degree of tension they impose on the network. Agents update their connections to decrease the total energy (tension) of the system, on average. Our approach is to consider a variable energy ε∈[0,1] for triads which are entirely made of negative relations. We show that the final state of the system depends on the initial density of the friendly links ρ_{0}. For initial densities greater than an ε-dependent threshold ρ_{0}^{c}(ε), a unipolar (paradise) state is reached. However, for ρ_{0}≤ρ_{0}^{c}(ε), multipolar and bipolar states can emerge. We observe that the number of stable final poles increases with decreasing ε where the first transition from bipolar to multipolar society occurs at ε^{*}≈0.67. We end the paper by providing a mean-field calculation that provides an estimate for the critical (ε dependent) initial positive link density, which is consistent with our simulations.

Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules

A malicious data miner can infer users’ private information in online social networks (OSNs) by data mining the users’ disclosed information. By exploring the public information about a target user (i.e. an individual or a group of OSN users whose privacy is under attack), attackers can prepare a training data set holding similar information about other users who openly disclosed their data. Using a machine learning classifier, the attacker can input released information about users under attack as non-class attributes and extract the private information as a class attribute. Some techniques offer some privacy protection against specific classifiers;, however, the provided privacy can be under threat if an attacker uses a different classifier (rather than the one used by the privacy protection techniques) to infer sensitive information. In reality, it is difficult to predict the classifiers involved in a privacy attack. In this study, we propose a privacy-preserving technique which first prepares a training data set in a similar way that an attacker can prepare and then takes an approach independent of the classifiers to extract patterns (or logic rules) from the training data set. Based on the extracted rule set, it then suggests the target users to hide some non-class attribute values and/or modify some friendship links for protecting their privacy. We apply our proposed technique on two OSN data sets containing users’ attribute values and their friendship links. For evaluating the performance of the proposed technique, we use conventional classifiers such as Na $$\ddot{\text {i}}$$ ve Bayes, Support Vector Machine and Random Forest on the privacy-protected data sets. The experimental results show that our proposed technique outperforms the existing privacy-preserving algorithms in terms of securing privacy while maintaining the data utility.

Philanthropic Actions Via Facebook During Covid-19 Pandemic: A Study of Digital Ethnography

Philanthropic movements to help society affected by Covid-19 has emerged massively nowadays. One of the movements is done by Dimas Jayasrana and friends in Lumbung Pangan via social media. They give help by donating leftovers, such as a handful of onions, vegetables leftovers from the fridge, sauce from fast food restaurants, salt, spices, and seasoning collected and given to the needy. The purpose of this research is to know how social media is used to increase people's awareness to participate in philanthropic actions. With the qualitative approach and digital ethnography method in the Facebook account of Lumbung Pangan founding father. The result of this research shows that interpersonal communication and friendship links in social media have played a role in delivering information and moving people to participate in philanthropies. The social movements in Lumbung Pangan have been continuing until today. Hence, the society in Jakarta such as senior citizens and the poor are greatly helped getting supplies for free. All walks of life, relatives and unknown people participate actively to give donation. As the result, Lumbung Pangan has developed corporation with farmers from Cianjur, called Rukun Sayur Makmur. They directly connect the produce to the buyers without broker.

О СОЦИАЛЬНОЙ ПРОНИЦАЕМОСТИ ГРАНИЦ МУНИЦИПАЛЬНЫХ ОБРАЗОВАНИЙ

The article investigates the intensity of social interaction between the population of different municipalities in Russian Federation. This interaction is estimated using user profile data from online social networks: a data source that grows more and more popular in population geography studies. We build a qualitative indicator of social interaction using data on the number of interregional friendship links for «VKontakte» social network users that live in different municipalities and have an open social network profile. We show that the intensity of social interaction is higher on the average for municipalities that share a common border. Social interaction intensity data is then used to characterize the permeability of municipality borders. We choose the least permeable borders among all border segments of municipalities in Russian Federation: those with the smallest transborder interaction intensity values and describe the social barriers that are revealed this way. We describe various configurations of these barriers and suggest hypotheses for the reasons why such barriers appear or do not appear. We also analyze several groups of socially insular groups of municipalities in the Khanty- Mansi Autonomous Okrug and the Yamalo-Nenets Autonomous Okrug and suggest possible reasons for their insularity.

Object Recommendation based Friendship Selection (ORFS) for navigating smarter social objects in SIoT

Social Internet of Things (SIoT) paradigm contributes for the social relationship between the objects. SIoT autonomously handles diverse set of objects, performing similar responsibilities. Providing user friendly smarter objects for resource sharing is a prerequisite in any SIoT platform. An effective service discovery depends on multiple attributes of object centrality. In this work, an efficient Object Recommendation based Friendship Selection (ORFS) model for network navigability and social relationship management among smarter objects in SIoT is proposed. The proposed ORFS model empowers to create, communicate and manage trust based social objects. A Grey Wolf Algorithm based User Object Affiliation (GWA-UOA) mechanism for the Smarter Object Recommendation (SOR) is proposed. Then Object Friendship Selection (OFS) through network navigability using Maximum Ranked Neighborhood (MRN) approach is proposed. Finally, desired service is navigated through the established friendship link. The proposed social-driven relationship approach is validated using two distinct real-world datasets. Experimental results proved that the proposed ORFS model well performed for navigating smarter social objects in SIoT. ORFS model achieved an improved performance with various factors like MAE, RMSE, computational time, average path length, recall, precision and F1 score.

Social connectedness and tourism demand

ABSTRACT This study employs a new measure of social connectedness, based on Facebook friendship links, to investigate the relationship between social networks and tourism demand. Using a standard gravity model and a sample of 39 destination countries and 167 origin countries, we document a positive, and highly significant, relationship between social connectedness and tourism demand. The results remain robust when we control for factors that may affect tourist flows between countries. Our findings bear important implications for tourism policy makers and professionals in their effort to attract visitors from origin countries.

Creative friendship and political diversity in Catalonia

The atmosphere of political confrontation in Catalonia has recently had a considerable impact in WhatsApp groups analyzed within the framework of the IMPACT-EV (Evaluating the impact and outcomes of EU SSH research) project (FP7). We developed a classification of four impacts. (a) In most cases where pro-independence supporters and Spanish nationalists have participated in WhatsApp groups, conversations about Catalan independence have broken groups up due to the groups being deleted, someone being removed, or someone leaving the group. (b) Most groups that have agreed to not speak about the issue have continued without conflict. (c) Groups that have been maintained have survived because their members hold the same political position. (d) Only some groups have persisted despite discussing the topic due to a respect for differing political positions. In this article, we analyze this reality from the conversations of different WhatsApp groups, and by employing the communicative methodology of research, we identify a predominance of links of instrumental friendship in Typology a and links of democratic friendship in Typology d.

Where Russian Online Nationalists Go When Their Communities are Banned: A Case Study of Russian Nationalism

This article presents the results of a case study of the online community Russian Nationalism, one of the most popular Russian nationalists’ online communities on VKontakte in 2012–2016. The article aims to find out where network leaders and common members of nationalist online groups closed on the Russian Internet go and in which thematic communities they continue their activity. Russian Nationalism members’ personal profiles and friendly links on VKontakte were used as data sources. The metrics of social network analysis were used to study Russian Nationalism’s network structure. The study showed that the ban of a massive extreme right nationalist online group leads to an increase in the number of smaller shelter groups that retain their ideological basis, but abandon the clearly expressed hate speech; members of a banned nationalist group flow to groups whose content is both close (militaristic, cultural, and historical) and far (everyday) to the extreme right. The content of the latter can transform in line with the nationalist ideology. The case study of Russian Nationalism shows a high potential for diffusion of extreme right nationalist ideas in the Russian network space, which manifests in their ability to penetrate other thematic and ideological areas of public discourse.

The consequences of friendships: Evidence on the effect of social relationships in school on academic achievement

This paper examines the impact of youth friendship links on student's own academic performance (grade point average) using the Add Health. We estimate a reduced form, high dimensional fixed effects model of within cohort or grade friendship links and use this model to predict each student's number of friends whose mothers completed four years of college. The effects of friendship links are identified using across-cohort, within school variation in demographic composition of the student's cohort or grade. We find that increases in the number of friendship links with students whose mothers are college educated raises grade point average among girls, but not among boys. Additional analyses suggest a positive view of the school environment and a perception of one's self as functioning well in that environment as possible mechanisms. The effects are relatively broad based across students over maternal education, racial and ethnic composition, and across schools that vary in demographic composition.

Achievement in student peer networks: A study of the selection process, peer effects and student centrality

Isolating the homophily effect from the socialization process is crucial in understanding the links between student achievement and peer performance. Then, the combined effects on student achievement of peer performance and of student centrality within a network have been scarcely studied. While friendship links have been addressed, researches rarely investigated instrumental ties. In this paper, 120 college students were asked about their friends and about their advisors. Results show a selection effect (i.e., students reassemble with friends sharing same levels of performance), and a socialization process (i.e., peer performance is significantly associated with later student achievement). While the centrality univariate effects are significant for student performance, only being a prestigious friend remains important when peer performance is taken into account.

SOCIAL MEDIA MARKETING INFLUENCE ON BRAND EQUITY AND IMPACT ON INTENTION TO BUY IN FASHION MARKETING

Topicality. Social media and web technologies allow customers to interact and communicate. Companies use social networks to promote their brand information. with the use of technologies Web 2.0 and later companies radically changed marketing processes and switched from interacting with groups of anonymous consumers to targeted interaction with specific consumers, for which it is possible to collect data about advantages, preferences, other purchases, interests and circles of communication. Big brands tend to connect with their customers through social media channels. Social networking sites such as Facebook, Twitter and MySpace, etc., have a significant impact on the lives of customers and their consumer and information experiences. Social media has a direct impact on how people communicate, exchange information, maintain their profiles, and establish relationships with others. Fashion industry is an important and growing element of modern consumer markets. As competition among fashion brands becomes more intense, offering many alternatives to customers, brand prosperity no longer seems to be guaranteed. The importance of managing clients as valuable assets of brands is no longer underlined. In this regard, it is of interest to explore the use of social media marketing tools (SMM) by fashion industry companies.Aim and tasks. The purpose of the article is to describe the peculiarities of using SMM tools industry companies to implement marketing policies by creating and developing brand capital and intent to purchase.Research results. The conceptual principles and purposes of using marketing of social networks for companies of the fashion industry are considered. On the basis of the analysis, it was established that, firstly, SMM activities of fashion brands include five elements; entertainment, interaction, tendency, adjustment and publicity (word of mouth). SMM branding, relies on and promotes values that are different from those of traditional marketing. Second, SMM's customer-perceived activity is important for all brand capital drivers. Since these measures for trendy brands are valid for all engines, they are quite effective. As an integrated marketing environment, SMM's activity effectively increases value capital by providing customers with a new value that is traditionally not available on ordinary media channels. The platforms of social media brands provide customers with the opportunity to establish sincere and friendly links with the brand and other users, so the brand's on-the-go social communications have had a positive impact on equity and equity of the brand.Conclusions. In comparing the influence of three customer equity drivers on purchase intention, value equity, and brand equity, this study found significant positive impacts. The findings of this study not only draw attention to the definition of brand equity capital but also point to opportunities and difficulties in determining future customer behavior. The conceptual foundations, mechanisms, tools and practice of applying SMM are the subject and prospects for further research in this direction.

Social Connectedness: Measurement, Determinants, and Effects

Social networks can shape many aspects of social and economic activity: migration and trade, job-seeking, innovation, consumer preferences and sentiment, public health, social mobility, and more. In turn, social networks themselves are associated with geographic proximity, historical ties, political boundaries, and other factors. Traditionally, the unavailability of large-scale and representative data on social connectedness between individuals or geographic regions has posed a challenge for empirical research on social networks. More recently, a body of such research has begun to emerge using data on social connectedness from online social networking services such as Facebook, LinkedIn, and Twitter. To date, most of these research projects have been built on anonymized administrative microdata from Facebook, typically by working with coauthor teams that include Facebook employees. However, there is an inherent limit to the number of researchers that will be able to work with social network data through such collaborations. In this paper, we therefore introduce a new measure of social connectedness at the US county level. Our Social Connectedness Index is based on friendship links on Facebook, the global online social networking service. Specifically, the Social Connectedness Index corresponds to the relative frequency of Facebook friendship links between every county-pair in the United States, and between every US county and every foreign country. Given Facebook’s scale as well as the relative representativeness of Facebook’s user body, these data provide the first comprehensive measure of friendship networks at a national level.