scholarly journals Modelling the Diffusion of Investment Decisions on Modular Social Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiaokang Cheng ◽  
Narisa Zhao
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Diffusion Model ◽  
Financial Market ◽  
Negative Impact ◽  
Investment Decisions ◽  
Negative Influence ◽  
Clustering Coefficient ◽  
The Impact

In the financial market, information and investment behaviors disseminate in investor social networks, and different contagion patterns may cause diverse investment trends. Prior studies have investigated the impact of investor social networks, but few have considered community structure. In this paper, we study the impact of the community structure of investor social networks on the diffusion of internet investment products. A two-stage diffusion model is proposed, and the clustering coefficient and modularity of an investor social network are considered. The results show that both modularity and the clustering coefficient have an impact on the diffusion velocity and scale and that the impact is most evident at the stage of explosive growth. The negative influence of a large modularity can be hardly mitigated by adjusting other factors. Furthermore, a decrease in modularity and an increase in the clustering coefficient can better facilitate diffusion when the temporary investment rate is high and can partly offset the negative impact of information discarding and divestment.

scholarly journals Functional brain network architecture supporting the learning of social networks in humans

2019 ◽  
Author(s):  
Steven Tompson ◽  
Ari E Kahn ◽  
Emily B. Falk ◽  
Jean M Vettel ◽  
Danielle S Bassett
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Brain Network ◽  
Temporoparietal Junction ◽  
Functional Brain ◽  
Network Learning ◽  
Functional Brain Network ◽  
The Social ◽  
The Impact

Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. From the behavioral data in both tasks, we found that learners were sensitive to the community structure of the networks, as evidenced by a slower reaction time on trials transitioning between clusters than on trials transitioning within a cluster. From the neuroimaging data collected during the social network learning task, we observed that the functional connectivity of the hippocampus and temporoparietal junction was significantly greater when transitioning between clusters than when transitioning within a cluster. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions during the social task than during the non-social task. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.

TO THE QUESTION OF THE IMPACT OF CLIMATE CHANGE ON THE ECOLOGICAL STATE OF ARTIFICIAL PLANTATIONS OF PITSUNDSKAYA PINE IN THE SEASIDE AREA OF GELENDZHIK

2017 ◽  
Author(s):  
Elizaveta Derevenets ◽  
Elizaveta Derevenets
Keyword(s):  
Negative Impact ◽  
Standard Technique ◽  
Negative Influence ◽  
Exhaust Gases ◽  
The Road ◽  
Industrial Enterprises ◽  
Federal Highway ◽  
On The Road ◽  
The Impact ◽  
The City

Gelendzhik is the resort town, there aren't a lot of industrial enterprises here. The main pollutant is transport. The work purpose is the assessment of a condition of the artificial landings of a pine located along the Federal highway "Don" and landings, which is nearly the sea coast. Researches were conducted to a standard technique of the General vital state (A. S. Bogolyubov). The assessment of a condition of pines was carried out during 6 years: from 2010 to 2015. For carrying out research we used 6 experimental grounds on the Markotkhsky spine and 2 control grounds within the town. We investigated 24 trees on each platform, middle age of the trees were 30 - 40 years. Results. 1. The condition of trees in the pine forests located in immediate proximity with the Federal highway "Don" (No. 1, 2, 3) is unsatisfactory. As even weak influences of the majority of atmospheric gaseous pollutants (sulphurous gas, nitrogen oxides, etc.) give effect of a necrosis and hloroz of pine needles, the condition of pines is connected with technogenic pollution. So near the Federal highway "Don" the air environment is strongly polluted by exhaust gases. Information of 2012 confirm that negative influence of the route on Markotkh's vegetation decreases at reduction of load of the route. 2. On the sites located above on a slope (No. 4,5,6) thanks to remoteness and the wind mode intensity of influence of pollutants is lower and a condition of pines the quite satisfactory. 3. Trees on the sites located near the sea (No. 7,8) are in a good shape. Small deterioration of a state is noted in very droughty years. Conclusion. Results of six years' research show that the condition of the plantings which are in close proximity with the road worsens. It is explained by increase in intensity of the movement on the road, especially during a resort season. Gelendzhik is the city with a good ecological shape, but the damage to environment is already caused. If not to take measures, we can lose a unique part of the nature in the future, recreate it will be impossible. Measures of reduction of negative impact of exhaust gases were offered. Results of researches are transferred to ecological department of the City administration of Gelendzhik.

TO THE QUESTION OF THE IMPACT OF CLIMATE CHANGE ON THE ECOLOGICAL STATE OF ARTIFICIAL PLANTATIONS OF PITSUNDSKAYA PINE IN THE SEASIDE AREA OF GELENDZHIK

2017 ◽  
Author(s):  
Elizaveta Derevenets ◽  
Elizaveta Derevenets
Keyword(s):  
Negative Impact ◽  
Standard Technique ◽  
Negative Influence ◽  
Exhaust Gases ◽  
The Road ◽  
Industrial Enterprises ◽  
Federal Highway ◽  
On The Road ◽  
The Impact ◽  
The City

Gelendzhik is the resort town, there aren't a lot of industrial enterprises here. The main pollutant is transport. The work purpose is the assessment of a condition of the artificial landings of a pine located along the Federal highway "Don" and landings, which is nearly the sea coast. Researches were conducted to a standard technique of the General vital state (A. S. Bogolyubov). The assessment of a condition of pines was carried out during 6 years: from 2010 to 2015. For carrying out research we used 6 experimental grounds on the Markotkhsky spine and 2 control grounds within the town. We investigated 24 trees on each platform, middle age of the trees were 30 - 40 years. Results. 1. The condition of trees in the pine forests located in immediate proximity with the Federal highway "Don" (No. 1, 2, 3) is unsatisfactory. As even weak influences of the majority of atmospheric gaseous pollutants (sulphurous gas, nitrogen oxides, etc.) give effect of a necrosis and hloroz of pine needles, the condition of pines is connected with technogenic pollution. So near the Federal highway "Don" the air environment is strongly polluted by exhaust gases. Information of 2012 confirm that negative influence of the route on Markotkh's vegetation decreases at reduction of load of the route. 2. On the sites located above on a slope (No. 4,5,6) thanks to remoteness and the wind mode intensity of influence of pollutants is lower and a condition of pines the quite satisfactory. 3. Trees on the sites located near the sea (No. 7,8) are in a good shape. Small deterioration of a state is noted in very droughty years. Conclusion. Results of six years' research show that the condition of the plantings which are in close proximity with the road worsens. It is explained by increase in intensity of the movement on the road, especially during a resort season. Gelendzhik is the city with a good ecological shape, but the damage to environment is already caused. If not to take measures, we can lose a unique part of the nature in the future, recreate it will be impossible. Measures of reduction of negative impact of exhaust gases were offered. Results of researches are transferred to ecological department of the City administration of Gelendzhik.

scholarly journals Emergence and evolution of social networks through exploration of the Adjacent Possible space

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Enrico Ubaldi ◽  
Raffaella Burioni ◽  
Vittorio Loreto ◽  
Francesca Tria
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Interactions ◽  
Clustering Coefficient ◽  
Human Beings ◽  
Broad Distribution ◽  
Phone Calls ◽  
Dynamical Features ◽  
Twitter Users ◽  
American Physical

AbstractThe interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls network.

IMPACT OF FACEBOOK ADDICTION DISORDER (FAD) ON STUDY HABITS AND ACADEMIC ACHIEVEMENT OF ADOLESCENTS

2021 ◽  
pp. 195-207
Author(s):  
Shivani Vashishtha ◽  
Sona Ahuja ◽  
Mani Sharma
Keyword(s):  
Academic Achievement ◽  
Social Networking Sites ◽  
Negative Impact ◽  
Age Groups ◽  
Negative Influence ◽  
Study Habits ◽  
Study Habit ◽  
Significant Negative Impact ◽  
Facebook Addiction ◽  
The Impact

With the present era being technology driven, social media has become an indispensable part of many people irrespective of their age. Among different age groups, the maximum users are adolescents and among different social networking sites (SNS), Facebook shares the major part of usage by them. Many adolescents are tending towards excessive usage of Facebook leading to its addiction. Does this addiction have negative influence on adolescents or it actually helps them to keep up with their counterparts and be socially connected to them for their betterment? This question is unanswered specifically when it concerns the impact that it has on the study habits and academic achievement of adolescents. The hypotheses were tested in order to explore the impact of six dimensions (mood modification, deficient self-regulations, salience, loss of control, withdrawal, and relapse) of Facebook Addiction Disorder (FAD) using Bergens' Facebook Addiction Scale (BFAS), developed by Andreassen (2012). The results are based on the survey conducted on 200 adolescents studying in different schools of India. The findings indicate that there is a significant negative impact of Facebook Addiction Disorder (FAD) on study habits and academic achievement of adolescents. The major implication derived is that the higher the addiction to Facebook the study habit become poor and academic achievement decreases resulting in lower grades.

scholarly journals I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13971-13972
Author(s):  
Yang Qi ◽  
Farseev Aleksandr ◽  
Filchenkov Andrey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Model Performance ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Modal Data ◽  
Social Media Networks ◽  
The Impact

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

scholarly journals Personality and the Impact of Celebrity Endorsements on Instagram

2021 ◽  
Vol 18 (3) ◽  
pp. 468-474
Author(s):  
Yair Amichai-Hamburger ◽  
Shir Etgar ◽  
Hadar Gil-Ad ◽  
Michal Levitan-Giat ◽  
Gaya Raz
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Locus Of Control ◽  
Strong Influence ◽  
Online Social Network ◽  
Strong Impact ◽  
Celebrity Endorsements ◽  
Internal Locus Of Control ◽  
The Impact ◽  
The Relationship

Celebrities are famous people who often belong to entertainment industry. They are known to have a strong influence on people’s behavior. In the digital age this impact has expanded to include the online arena. Celebrities increasingly utilize Instagram, an online social network, to promote commercial products. It is important to learn to what extent people are influenced by this type of promotion and what sort of people are likely to be swayed by it. Research has demonstrated that people’s personalities have a strong impact on their behaviors online. However, until now, these investigations have not included the relationship between personality and the degree of celebrity influence through social networks. This study examines how much the personality of a user is related to the degree to which he or she is influenced by these Celebrity Instagram messages. Participants comprised 121 students (34 males, 87 females). They answered questionnaires which focused on their personality and were asked about the degree of influence celebrities exerted upon them through Instagram. Results showed that people who are characterized as being open and having an internal locus of control are more resistant to such celebrity influences. This paper demonstrates that the personality of a recipient is likely to influence the degree of impact that a celebrity endorsement is likely to produce. The implications of these results are discussed.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Some results of studying the impact of digital technologies upon learning activities

2020 ◽  
pp. 1-14
Author(s):  
Elvira Vitaljevna Burtseva ◽  
Olga Chepak ◽  
Olga Kulikova
Keyword(s):  
Higher Education ◽  
Social Networks ◽  
Negative Impact ◽  
Digital Technologies ◽  
Educational Process ◽  
Learning Activities ◽  
Key Factor ◽  
Negative Effect ◽  
The Subject ◽  
The Impact

The subject of this research is the implementation of digital technologies in educational process of a university. The goal consists in studying the impact of digital technologies upon the students’ learning activities. The article presents the results of questionnaire-based survey among students by the three question pools. In the course of research, the author examines such aspects of the problem, as the positive and negative impact of technologies upon learning activities of the students of digital generation. Particular attention is given to consideration of students’ attitude on digitalization of higher education. The opinions of pedagogues on the results of conducted research are presented. The scientific novelty lies in mainstreaming the question on the negative impact of digital technologies upon learning activities of the modern generation of students that deserves special attention. On the background of common passion of the scholars of researchers and pedagogues for the ideas of digitalization of education, when digital technologies are viewed as virtually the key factor for modernization of educational process; second come the problems of growing pathological dependence of youth on digital technologies, undesired to switch to digitalized educational process to the disadvantage of communication in social networks and pleasant pastime online. The problem of the negative effect of digital technologies on learning activities must be recognized in order to find the ways for its solution.

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