Common Friends Research Articles

Friendship jealousy and interaction needs: how mutual friend features affect users of WeChat Moments.

Many social networking services (SNSs) have features that highlight the common friends of pairs of users. Previous research has examined recommendation systems that use mutual friend metrics, but few scholars have studied how the existence of features related to mutual friends affects users in SNSs. To explore this issue further, we conducted interviews with 22 users of WeChat Moments to investigate how certain rules involving mutual friends affect users and how they deal with the issues that arise due to these rules. We found that the three Moments rules related to mutual friends (response visibility, response notifications, and information dissemination) can cause users to feel jealous, annoyed, and embarrassed. To prevent these negative experiences, users may reduce the amount of information they disclose or the frequency of their interactions in SNSs. Based on these findings, we propose several future directions for scholars and a small number of design suggestions aimed at assisting providers to satisfy users' interaction needs.

The Utilization of Instructional Materials in Teaching-Reading of Primary Education Children in Selected Government Schools

The primary intention of this study is to determine the level of effectiveness of different instructional materials used in teaching reading to primary education children of selected government schools in China. The descriptive research method was employed in this study. Descriptive research, also known as statistical research, describes data and characteristics about the population or phenomenon being studied. Upon approval of the concerned individuals, questionnaires were reproduced and distributed through online forms to the seventy eight (78) teacher - respondents.The researcher used the convenience sampling in choosing the respondents because some teachers in the schools she selected were here classmates and common friends .It would be more convenient for the researcher to collect necessary data if she is acquainted with her respondents. As to effectiveness of different instructional materials commonly used in teaching reading, most of the respondents believed that most of the millennial students are visual learners hence the variety of materials used in the classroom is proven effective in teaching reading.

Who are the best voluntary disseminators on social networking sites? An exploration based on panel data from twitter

PurposeUsers' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.Design/methodology/approachVia Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.FindingsPreference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.Originality/valueThis work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

Dual Structural Consistency Preserving Community Detection on Social Networks

Community detection on social networks is a fundamental and crucial task in the research field of social computing. Here we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DSCPCD</i> —a dual structural consistency preserving community detection method to uncover the hidden community structure, which is designed regarding two criteria: 1) users interact with each other in a manner combining uncertainty and certainty; 2) original explicit network (two linked users are friends) and potential implicit network (two linked users have common friends) should have a consistent community structure, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dual structural consistency</i> . Particularly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DSCPCD</i> formulates each user in a social network as an individual in an evolutionary game associated with community-aware payoff settings, where the community state evolves under the guidance of replicator dynamics. To further seek each user's membership, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">happiness</i> index to measure all users' satisfaction towards two community structures in explicit and implicit networks, meanwhile, the dual community structural consistency between the two networks is also characterized. Specifically, each user is assumed to maximize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">happiness</i> bounded by the evolutionary community state. We evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DSCPCD</i> on several real-world and synthetic datasets, and the results show that it can yield substantial performance gains in terms of detection accuracy over several baselines.

The Effects of Individuals’ Opinion and Non-Opinion Characteristics on the Organization of Influence Networks in the Online Domain

The opinion dynamics literature argues that the way people perceive social influence depends not only on the opinions of interacting individuals, but also on the individuals’ non-opinion characteristics, such as age, education, gender, or place of residence. The current paper advances this line of research by studying longitudinal data that describe the opinion dynamics of a large sample (~30,000) of online social network users, all citizens of one city. Using these data, we systematically investigate the effects of users’ demographic (age, gender) and structural (degree centrality, the number of common friends) properties on opinion formation processes. We revealed that females are less easily influenced than males. Next, we found that individuals that are characterized by similar ages have more chances to reach a consensus. Additionally, we report that individuals who have many common peers find an agreement more often. We also demonstrated that the impacts of these effects are virtually the same, and despite being statistically significant, are far less strong than that of opinion-related features: knowing the current opinion of an individual and, what is even more important, the distance in opinions between this individual and the person that attempts to influence the individual is much more valuable. Next, after conducting a series of simulations with an agent-based model, we revealed that accounting for non-opinion characteristics may lead to not very sound but statistically significant changes in the macroscopic predictions of the populations of opinion camps, primarily among the agents with radical opinions (≈3% of all votes). In turn, predictions for the populations of neutral individuals are virtually the same. In addition, we demonstrated that the accumulative effect of non-opinion features on opinion dynamics is seriously moderated by whether the underlying social network correlates with the agents’ characteristics. After applying the procedure of random shuffling (in which the agents and their characteristics were randomly scattered over the network), the macroscopic predictions have changed by ≈9% of all votes. What is interesting is that the population of neutral agents was again not affected by this intervention.

Triangles, Major Powers, and Rivalry Duration

Why do some international rivalries last longer than others? Rivalry literature focuses on the dyadic interactions within rivalries, but most rivalries have been connected to other rivalries. This paper examines a rivalry’s overlapping immediate strategic environment focusing on the countries directly connected via rivalries or alliances. Specifically, I consider how rivals are connected to third parties as common enemies, common friends, or mixed. I focus on three mechanisms to explain the third parties’ influence on the focal rivalry. First, a rivalry is more likely to end when it has common allies (information flow) or common enemies (prioritization). Second, a rivalry with mixed third parties is more likely to endure (stable blocs). Analyses of rivalry duration show that the duration of minor-minor rivalries is more likely to be influenced by common allies and mixed third parties, while that of major-minor rivalries is more likely to be affected by common enemies.

Predicting future link in social network

A social network can have many different types of links or margins between nodes. Those, for example, could be social contacts, major contacts, or calls. Link Prediction is the problem of predicting edges that may not be present in a given or present time, but that have not yet been discovered and may occur in the near future. We are developing predictive linking methods based on step-by-step analysis of network nodes. Consider a network of collaborative writing among scientists, e.g. The two scientists closest to the network will have similar colleagues, so they may be working together soon. Our goal is to make this accurate idea more accurate and to understand what steps to take to approach the network that lead to the most accurate prediction of a link. Link prediction algorithms can be divided into three categories: Node Neighborhood Mode, Mode-based Mode, and Meta Mode. The node location method is based on network location features, which focuses mainly on the node structure (i.e., based on the number of common friends shared by two users). Local-based measures are: general neighborhood, Jaccard Coefficient, Adamic/Adar, and preferred attachments. P

Activity-based Friend Recommendation System (ARS) in Location-based Social Network

Common friend and place recommendation services in Location-based Social Network (LBSN) is based on user’s location tracking. However, since each user can do different activities even in the same place, location data is not enough to provide accurate recommendation for LSBN. To address this problem, Activity-based friend and place Recommendation System (ARS) is proposed. ARS considers two additional factors to improve recommendation accuracy: time and activity. ARS collects the time-related activity and location data from users through the developed scheduler application and then performs the recommendation for users based on the calculated similarity among them. Performance evaluation shows that ARS can provide accurate recommendation between users who have similar activity and location patterns according to time.

Турция и Азербайджан: одна нация – одно государство?

The article scrutinizes Turkish-Azerbaijani key areas of cooperation – political, economic, military-technical, cultural and humanitarian. For instance, in addition to intensive interaction at the level of high-ranking political elites, Azerbaijani-Turkish unity in the political context is largely built on the basis of classifying foreign policy partners into common friends and common enemies (the “friend-foe” principle). Thus, both Ankara and Baku are building friendly relations with the Turkic states of Central Asia. At the same time, Armenia acts as a common “enemy”. In the military sphere, both states seek to form Turkey-oriented militarized structures and, moreover, to create a unified Turkic army in the long term. The nature of economic relations between Azerbaijan and Turkey is largely based on a competitive basis. Energy acts as a unifying and at the same time a divisive factor since both states strive for leadership in this area, seeking to occupy a special place in the regional energy market. In the cultural and humanitarian sphere, the unfolding Turkey-centric integration is of particular importance. The author comes to the conclusion that nowadays the bilateral relations between Turkey and Azerbaijan are characterized by the “merging” of the two countries into a single Turkey-centric space, which manifests in almost all fields of interaction. The Republic of Turkey seeks to influence Baku in order to make it a reference point of the “Turkic world” under its auspices. This trend is most likely to strengthen in the future.

Experiences of Online COVID-19 Information Acquisition among Persons with Type 2 Diabetes and Varying eHealth Literacy.

During the COVID-19 pandemic, the Internet has been a major source of information for people to keep updated with news and guidelines. However, concerns have been raised about the ‘infodemic’, which includes the overabundance of online information and the spread of misleading information. Adequate eHealth literacy skills among world citizens have therefore been emphasized as vital during the pandemic. Persons with type 2 diabetes have been at increased risk of severe outcomes of COVID-19 disease. This study aimed to explore online COVID-19 information acquisition experiences among persons with type 2 diabetes and varying eHealth literacy. Fifty-eight participants filled out the eHealth Literacy Scale (eHEALS), along with a qualitative questionnaire with free-text questions. Additionally, 10 participants were interviewed. Thematic analysis was applied to identify patterns in participants’ experiences. Two domains were identified: perceived challenges with online information about COVID-19, and coping strategies to manage challenges. The perceived challenges were: being exposed to information overload, dealing with conflicting information, and being strongly emotionally affected. The related coping strategies were: protecting oneself, trusting authorities, taking command, and using common sense. These strategies often involved triangulation of the information obtained, including participants consulting their common sense, various sources, or family and friends. This paper highlights the crucial role of authorities in delivering online information, that according to health literacy principles, is easy to access, understand, and use. Furthermore, our results reinforce the importance of diabetes nurses, as well as healthcare professionals in general, in encouraging patients to share their Internet findings, promote information from reliable sources, and deliver tailored information that suits individual needs. Because our results underline the importance of social support in eHealth literacy and the assessment of online health information, the inclusion of family and friends needs to be increasingly considered in diabetes care. Due to the small homogenous sample, the results of this study cannot be generalized. However, the reader can assess the transferability to other situations and settings based on our contextual descriptions.

An adaptive group decision making framework: Individual and local world opinion based opinion dynamics

Opinion dynamics (OD) models, which simulate individuals’ opinion evolution process on social network to analyze the final state of opinion distribution in a group, usually differ from each other due to the differences in social network evolution rules and opinion evolution rules. However, most existing social network evolution rules and opinion evolution rules usually cannot characterize the comprehensive influence of key factors such as neighbors and opinion differences in social relationships. To fully consider the properties of social network evolution and improve the efficiency of consensus reaching process in group decision making, this paper introduces the concept of local world opinion derived from individuals’ common friends, and then proposes an individual and local world opinion-based OD model. In the proposed model, social network evolution is jointly determined by the distance between individual opinions and network structure similarity. The pair of individuals with the largest consensus improvement space are then suggested to adjust their opinions by using an adaptive individual opinion adjustment mechanism. Finally, detailed simulation results are provided to demonstrate the convergence of the proposed model and analyze different parameters’ effects on the stabilized time steps and the number of stable state opinion clusters.

Teenage Pregnancy: Its Effects and Their Coping Strategies

Teenage pregnancy has been a societal problem over the last decades in various provinces in the Philippines. These incidents categorically hampered teenagers’ lives as they affected their wellbeing on becoming the young mother of their children. The study utilized a qualitative research design specifically a phenomenological approach. The selected pregnant teenagers of Barangay Lucena, Prosper dad, Agusandel Sur served as the Key Informants. The researchers used an interview guide questionnaire in the conduct of the interview. Then, it was recorded through an audio tape. Transcription and translation of the information were made. The information gathered was then interpreted through thematic analysis. Furthermore, the study revealed that teenage mothers experienced humiliations and criticism among their family, peers and people in the community. Most of them resorted into dropping out from school and others asserted that they chose to stay at home and avoided their common friends. Consequently, most of these teenage mothers were able to overcome the situation by accepting the responsibility as parents. They have asserted that they were willing to do everything for the welfare of their children. Indeed, one of the most emphasized factors was due to low family income. The families with low economic status were more vulnerable into teenage pregnancy incidents. Other factors were peer influence, lack of awareness on sex education and other family problems.

Friend's recommendation on social media using different algorithms of machine learning

Friends Recommendation System identifies the behavior of users found in the dataset like user having number of followers, number of followings, common friends between followers and followings and provides the friend suggestions for the users they can follow. Recommendation system can also be used in other areas like recommending webpages to users in searching engines like Google, Explorer, Microsoft Edge. Recommending music in wynk, video recommendation in you tube, movie recommendation in amazon prime, recommendation of products to purchase in e-commerce applications like Flipkart, Amazon. Machine learning is used for providing recommendation on social networking application like Facebook, Instagram, Twitter Etc., In this Paper, recommendation of friends is done for Facebook. Similarity Coefficient calculations can be done using Jaccard Distance, Cosine Distance. Ranking Measures are done using Page Rank. Measuring the F1-Score and comparing the accuracy of different machine learning algorithms. These helps in finding which algorithm is more accurate in providing friends recommendations in social media recommendation system.

Growth of common friends in a preferential attachment model

The number of common friends (or connections) in a graph is a commonly used measure of proximity between two nodes. Such measures are used in link prediction algorithms and recommendation systems in large online social networks. We obtain the rate of growth of the number of common friends in a linear preferential attachment model. We apply our result to develop an estimate for the number of common friends that can significantly reduce the cost of computing these measures. We also observe a phase transition in the limiting behavior of the number of common friends; depending on the range of the parameters of the model, the growth is either power-law, or, logarithmic, or static with the size of the graph.

TRANSLATION OF FALSE FRIENDS AMONG B2 LEVEL ENGLISH LANGUAGE LEARNERS

This paper investigates how successful B2 level English language learners (ELLs), high school students are in translating a group of most common false friends (FFs) from English to BCS and vice versa and examines whether they are more successful in translating absolute or partial FFs. In line with the classification by Otwinowska-Kasztelanic (2015), false friends are considered to be a class of cognates. They are further sub-classified into absolute FFs, which have the same or similar form and dissimilar meaning in two languages (e.g. eventually, meaning finally in English and eventualno, meaning possibly in BCS) and partial false friends with the same or similar form and one same and another dissimilar meaning. (e.g. argument in English, meaning reason, the same as argument in BSC and disagreement, the meaning for which a different word is used in BCS, rasprava). Due to their deceptive nature, FFs have been researched within different theoretical frameworks - theoretical, contrastive, applied linguistics, semantics, pragmatics and translation studies. By means of a survey and descriptive and inferential statistics, this paper confirms the hypothesis that there is a statistically significant difference between correct translation of FFs from English to BCS and their correct translation from BCS to English. Furthermore, the second hypothesis was also confirmed, namely that the B2 ELLs are more successful in translating partial than in translating absolute FFs. The research results suggest that in teaching FFs as items of deceptive vocabulary both explicit and implicit methods need to be applied

Does Facebook-enabled communication influence weak-tie relationships over time? A longitudinal investigation into mediated relationship maintenance

ABSTRACT Although presumed to be a means of maintaining relationships, there is limited empirical evidence whether relationships are actually maintained over time by Facebook-enabled communication. Drawing on communicate bond belong theory, we focus on the most common Facebook friends: weak ties. Adult (N = 174) participants completed three waves of data collection each a month apart, reporting on three non-kin relationships: one with routine Facebook communication, one without routine Facebook communication, and one maintained on other modalities outside Facebook. Latent growth curve analyses suggest although relationships differ in mean closeness, uniqueness, and commitment, Facebook-enabled communication was not associated with relational change. Results suggest the frequency of Facebook-enabled communication reflects relationship closeness but does not influence change in closeness over three months.

A generalized model for social collaboration networks

In this paper, we constructed a three-parameter general social collaboration network model (GCNM) using both similarity-based strategy and copying-based strategy. In this model, a connection can be introduced between two nodes if they are similar in attributes or similar in structure (i.e., have many common friends). This model can match various types of real-world social networks by just choosing different values for three parameters, and therefore, can reproduce observed real network characteristics. Particularly, three widely studied models (similarity-based, copying-based, and pure preferential attachment (PA)) are only the special cases of this new model. Specifically, this new model possesses following new characteristics which existing models have difficulty to fulfill in all: 1) it not only exhibits the high average clustering coefficient, but also the high global clustering coefficient and explicit community structure (great number of triangles); 2) it not only makes the connections between the new node and existing nodes, but also between existing nodes; and 3) it has not only linear, but also non-linear relationship between the linking probability and the degree (the connectivity of nodes). Numerical results, which are in good agreement with real-world datasets from different fields, demonstrated all of these.

The theory of collectivity of drinking cultures: how alcohol became everyone's problem.

The theory of collectivity of drinking cultures: how alcohol became everyone's problem.

AN ADVANCEMENT OF LINK ESTABLISHMENT IN SOCIAL NETWORK

The Study of Hierarchical Social Structures is Known as Social Network Analysis (SNA). Link Prediction is one of the most predominant field in Social Network analysis. The connection between two nodes in a Social Network is based on the Similarity function between the nodes which can be accomplished based on the number of common friends. Local Link Prediction Algorithm and Global Link prediction through considering of user’s activities in addition to not unusual paths. We have used the Social networking analysis to achieve the best performance from those.

Link Prediction in Complex Networks using Embedding Techniques and Similarity Measures

Networks have proved to be very helpful in modelling complex systems with interacting components. There are various problems across various domains where the systems can be modelled in the form of a network with links between interacting components. The Problem of Link Prediction deals with predicting missing links in a given network. The application of link prediction ranges across various disciplines including biological networks, transportation networks, social networks, telecommunication networks, etc. In this paper, we use node embedding methods to encode the nodes into low dimensional embeddings and predict links based on the edge embeddings computed by taking the hadamard product of the participating nodes. We further compare the accuracy of the models trained on different dimensions of embeddings. We also study how the introduction of additional features changes the accuracy when introduced to various dimensions of node embeddings. The additional features include overlapping measures such as Jaccard similarity, Adamic-Adar score and dot product between node embeddings as well as heuristic features i.e. Common Neighbors, Resource Allocation, preferential attachment and friend tns score.