Predicting the social influence of upcoming contents in large social networks

As societies modernize, they go through what has become known as “the demographic transition;” couples begin to limit the size of their families. Models to explain this change assume that reproductive behavior is either under individual control or under social control. The evidence that social influence plays a role in the control of reproduction is strong, but the models cannot adequately explain why the development of small family norms always accompanies modernization. We suggest that the widening of social networks, which has been found to occur with modernization, is sufficient to explain the change in reproductive norms if it is assumed that (a) advice and comment on reproduction that passes among kin is more likely to encourage the creation of families than that which passes among nonkin and (b) this advice and comment influence the social norms induced from the communications. This would, through a process of cultural evolution, lead to the development of norms that make it increasingly difficult to have large families.

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WhatsApp Web for academic purposes in times of covid-19

Apertura ◽

10.32870/ap.v13n2.2084 ◽

2021 ◽

Vol 13 (2) ◽

pp. 54-69

Author(s):

Nataly Andrea Guiñez-Cabrera ◽

◽

Katherine Mansilla-Obando ◽

Keyword(s):

Social Networks ◽

Social Network ◽

Social Influence ◽

Qualitative Methodology ◽

Unified Theory ◽

Communication Tools ◽

Use Of Technology ◽

The Social ◽

Facilitating Conditions ◽

Educational Field

One of the most used communication tools is WhatsApp, which increased its use due to covid-19, along with other social networks. In the educational field, students are also increasingly adopting this application for academic purposes from their computers called WhatsApp Web. However, more knowledge is needed about the factors that influence the acceptance and use of this social network. Therefore, the purpose of this study is to understand from the perspective of students the factors of acceptance and use of WhatsApp Web for academic purposes during the covid-19 pandemic. A qualitative methodology was used to achieve this objective, through fourteen semistructural interviews with students from various disciplines and universities. The findings of this study were analyzed with the unified theory of acceptance and use of technology (UTAUT). Where a fifth factor teamworkwas incorporated, being additional to the factors already existing in this theory (the expectation of performance, the expectation of effort, the social influence and the facilitating conditions). This study provides new insights as it is a pioneering research that UTAUT uses to interpret the acceptance and use of WhatsApp Web for academic purposes.

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Impact of sharing full versus averaged social information on social influence and estimation accuracy

Journal of The Royal Society Interface ◽

10.1098/rsif.2021.0231 ◽

2021 ◽

Vol 18 (180) ◽

pp. 20210231

Author(s):

Bertrand Jayles ◽

Clément Sire ◽

Ralf H. J. M. Kurvers

Keyword(s):

Social Networks ◽

Social Influence ◽

Recommender Systems ◽

Social Information ◽

Estimation Accuracy ◽

Human Ability ◽

The Social ◽

Recent Developments ◽

Available Information

The recent developments of social networks and recommender systems have dramatically increased the amount of social information shared in human communities, challenging the human ability to process it. As a result, sharing aggregated forms of social information is becoming increasingly popular. However, it is unknown whether sharing aggregated information improves people’s judgments more than sharing the full available information. Here, we compare the performance of groups in estimation tasks when social information is fully shared versus when it is first averaged and then shared. We find that improvements in estimation accuracy are comparable in both cases. However, our results reveal important differences in subjects’ behaviour: (i) subjects follow the social information more when receiving an average than when receiving all estimates, and this effect increases with the number of estimates underlying the average; (ii) subjects follow the social information more when it is higher than their personal estimate than when it is lower. This effect is stronger when receiving all estimates than when receiving an average. We introduce a model that sheds light on these effects, and confirms their importance for explaining improvements in estimation accuracy in all treatments.

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Crowd-Sourcing Information Dissemination Based on Spatial Behavior and Social Networks

Mobile Information Systems ◽

10.1155/2021/6652740 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yong Cheng

Keyword(s):

Social Networks ◽

Social Influence ◽

Information Dissemination ◽

Real Data ◽

Physical World ◽

Spatial Behavior ◽

Communication Model ◽

Information Communication ◽

Crowd Sourcing ◽

The Social

In the context of today’s network era, rich social networks and convenient network communication make different individuals and groups interact and transmit information in more diversified ways, which also bring new dissemination in information of crowd-sourcing tasks. The paper analyzes mobile behavior characteristics of users from different perspectives, such as spatial activity behavior and location type preference, and constructs a user space mobile behavior model based on the physical world. At the same time, it analyzes the social influence of users in social networks and mode of information transmission. In the paper, real data sets are adopted, mathematical modeling and computer simulation are combined to build an information communication model around the social influence of users in social networks, and the rules of information communication in new environment of social networks are depicted in combination with users’ spatial movement.

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Social Dollars in Online Communities: The Effect of Product, User, and Network Characteristics

Journal of Marketing ◽

10.1509/jm.16.0271 ◽

2018 ◽

Vol 82 (1) ◽

pp. 93-114 ◽

Cited By ~ 25

Author(s):

Eunho Park ◽

Rishika Rishika ◽

Ramkumar Janakiraman ◽

Mark B. Houston ◽

Byungjoon Yoo

Keyword(s):

Social Networks ◽

Social Influence ◽

Online Communities ◽

Theory And Practice ◽

Social Contagion ◽

Social Connections ◽

Network Ties ◽

Network Characteristics ◽

The Social ◽

Hedonic Products

Online communities have experienced burgeoning popularity over the last decade and have become a key platform for users to share information and interests, and to engage in social interactions. Drawing on the social contagion literature, the authors examine the effect of online social connections on users’ product purchases in an online community. They assess how product, user, and network characteristics influence the social contagion effect in users’ spending behavior. The authors use a unique large-scale data set from a popular massively multiplayer online role-playing game community—consisting of users’ detailed gaming activities, their social connections, and their in-game purchases of functional and hedonic products—to examine the impact of gamers’ social networks on their purchase behavior. The analysis, based on a double-hurdle model that captures gamers’ decisions of playing and spending levels, reveals evidence of “social dollars,” whereby social interaction between gamers in the community increases their in-game product purchases. Interestingly, the results indicate that social influence varies across different types of products. Specifically, the effect of a focal user's network ties on his or her spending on hedonic products is greater than the effect of network ties on the focal user's spending on functional products. Furthermore, the authors find that user experience negatively moderates social contagion for functional products, whereas it positively moderates contagion for hedonic products. In addition, dense networks enhance contagion over functional product purchases, whereas they mitigate the social influence effect over hedonic product purchases. The authors perform a series of tests and robustness checks to rule out the effect of confounding factors. They supplement their econometric analyses with dynamic matching techniques and estimate average treatment effects. The results of the study have implications for both theory and practice and help provide insights on how managers can monetize social networks and use social information to increase user engagement in online communities.

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Intention to purchase behavior on social e-commerce website across cultures (case study: Iranian online purchaser)

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v15i9.186 ◽

2016 ◽

Vol 15 (9) ◽

pp. 7077-7089

Author(s):

Ali Rezaeian ◽

Sajjad Shokouhyar ◽

Shahabedin Yousefi

Keyword(s):

Social Networks ◽

Social Media ◽

Social Identity ◽

Social Influence ◽

Cognitive Style ◽

Business Environment ◽

Customer Participation ◽

Social Commerce ◽

Intention To Purchase ◽

The Social

With the increasing popularity of social media, millions of users use social media services in the space such as Facebook, Twitter, MySpace, etc. Following that many organizations see this phenomenon as an opportunity to create new business and know this is known as social commerce. This phenomenon is not only due to the growth of social media, but is also because of users' participation in the fate of the marketing and sale of products. So, e-commerce has undergone a revolution, which is affected by the adoption of web 2 functionalities to increase customer participation and achieve greater economic value. Therefore, studying the behavior of buyers in the social commerce platforms can create more value for owners of e-commerce in the context of social commerce. For this reason, attempts were made to obtain more accurate findings regarding the behavior of e-commerce purchasers in social networks by taking into account the moderating influence of culture on it (Iranian online purchasers).This is an applied study. It also considered a descriptive cross-sectional study with regards to the way data are collected. The analysis of data collected from 184 active professionals in the IT industry and users of social networks indicates the moderating effect of culture and the mediating role of trust in a social network community in terms of social identity, trust transference (Familiarity), social influence (intimacy and friendship), cognitive style and the intention to purchase in the social business environment. Moreover, these findings also show that trust transference affects intention to purchase in social networks considering the aspects of familiarity, social identity and cognitive style. But the direct effect of social influence (feel close to) on the purchase intention has been rejected.

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Topic-based Social Influence Measurement for Social Networks

Australasian Journal of Information Systems ◽

10.3127/ajis.v21i0.1552 ◽

2017 ◽

Vol 21 ◽

Cited By ~ 3

Author(s):

Asso Hamzehei ◽

Shanqing Jiang ◽

Danai Koutra ◽

Raymond Wong ◽

Fang Chen

Keyword(s):

Social Networks ◽

Social Science ◽

Social Influence ◽

Science Studies ◽

User Generated Content ◽

Twitter Data ◽

User Influence ◽

The Social ◽

Influence Measurement ◽

Networks Structure

Social science studies have acknowledged that the social influence of individuals is not identical. Social networks structure and shared text can reveal immense information about users, their interests, and topic-based influence. Although some studies have considered measuring user influence, less has been on measuring and estimating topic-based user influence. In this paper, we propose an approach that incorporates network structure, user-generated content for topic-based influence measurement, and user’s interactions in the network. We perform experimental analysis on Twitter data and show that our proposed approach can effectively measure topic-based user influence.

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A Methodology for Evaluating Algorithms That Calculate Social Influence in Complex Social Networks

Complexity ◽

10.1155/2018/1084795 ◽

2018 ◽

Vol 2018 ◽

pp. 1-20

Author(s):

Vanja Smailovic ◽

Vedran Podobnik ◽

Ignac Lovrek

Keyword(s):

Machine Learning ◽

Social Networks ◽

Social Network ◽

Social Influence ◽

Online Social Networks ◽

Ground Truth ◽

Descriptive Statistics ◽

The Social ◽

Alternative Approaches

Online social networks are complex systems often involving millions or even billions of users. Understanding the dynamics of a social network requires analysing characteristics of the network (in its entirety) and the users (as individuals). This paper focuses on calculating user’s social influence, which depends on (i) the user’s positioning in the social network and (ii) interactions between the user and all other users in the social network. Given that data on all users in the social network is required to calculate social influence, something not applicable for today’s social networks, alternative approaches relying on a limited set of data on users are necessary. However, these approaches introduce uncertainty in calculating (i.e., predicting) the value of social influence. Hence, a methodology is proposed for evaluating algorithms that calculate social influence in complex social networks; this is done by identifying the most accurate and precise algorithm. The proposed methodology extends the traditional ground truth approach, often used in descriptive statistics and machine learning. Use of the proposed methodology is demonstrated using a case study incorporating four algorithms for calculating a user’s social influence.

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