scholarly journals The Brand-Generated Content Interaction of Instagram Stories and Publications: A Comparison between Retailers and Manufacturers

2020 ◽  
Vol 16 (3) ◽  
pp. 513-524
Author(s):  
Paloma de H. Sánchez-Cobarro ◽  
Francisco-Jose Molina-Castillo ◽  
Cristina Alcazar-Caceres
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Impact ◽  
Considerable Increase ◽  
Structural Equations ◽  
Communication Techniques ◽  
Content Interaction ◽  
The Mind ◽  
The Impact ◽  
The Relationship

The last decade has seen a considerable increase in entertainment-oriented communication techniques. Likewise, the rise of social networks has evolved, offering different formats such as publication and stories. Hence, there has been a growing interest in knowing which strategies have the greatest social impact to help position organizations in the mind of the consumer. This research aims to analyze the different impact that stories and publications can have on the Instagram social network as a tool for generating branded content. To this end, it analyses the impact of the different Instagram stories and publications in various sectors using a methodology of structural equations with composite constructs. The results obtained, based on 800 stories and publications in four different companies (retailers and manufacturers), show that the reach of the story generally explains the interaction with Instagram stories. In contrast, in the case of publications, impressions are of greater importance in explaining the interaction with the publication. Among the main contributions of the work, we find that traditional pull communication techniques have been losing effectiveness in front of new formats of brand content generation that have been occupying the time in the relationship between users and brands.

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 Measuring the value of the social network: analysis, quantitative approach, and application

2015 ◽  
Author(s):  
Sujata Jindal ◽  
Ritu Sindhu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Impact ◽  
Quantitative Approach ◽  
Social Data ◽  
The Social ◽  
The Impact ◽  
Day By Day

Social networks are growing day by day. Users of the social networks are generating values for these networks. All the users can’t be considered equal as they have different social network impact value. In this paper we analyze the social impact of a user and propose a method to estimate an individual’s worth to a social network in terms of impact. The mathematical evaluations show the effectiveness of our method. Based on the proposed method many applications can be built taking into consideration the impact any individual’s social profile has. We have tried to make various social data attributes more valuable and meaningful.

scholarly journals The Influence of a Profile in a Professional Social Network on the Development of a Specialist's Career

2021 ◽  
Vol 31 (1) ◽  
pp. 60-68
Author(s):  
Galina Nikolaeva ◽  
Valeriya Perekrestova ◽  
Aleksey Perekrestov ◽  
Polina Fursova
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Career Development ◽  
Career Advancement ◽  
Statistical Data ◽  
Negative Answer ◽  
Professional Networks ◽  
Personal Profile ◽  
The Impact ◽  
The Relationship

A successful career requires improving professional skills and developing business relationships. A personal profile in professional social networks and its proficient management can have a significant impact on career development. The article is devoted to investigating the impact of a specialists profile in professional social networks on the career development. The study analyses the statistical data on the use of social networks for recruiting. The relationship between a profile in professional social networks and the development of a specialist's career was investigated by conducting a sociological survey of networks users from various fields of activities. Most of the surveyed respondents (61 %) answered positively to the question about the benefits of professional social networks for career advancement, another part of the respondents (19 %) is not sure, but tends to answer positively. Only 11 % of the respondents are inclined to give a negative answer to this question and 1 % answered negatively, pointing out the uselessness of professional networks in their careers. Thus, the study confirmed the need to apply the profile for the development of a specialist's career. The advantages of a profile in professional networks are highlighted, allowing the development of effective professional communications: users of social professional networks, actively participating in forum discussions, publishing papers on the site, can attract attention of potential employers and develop their reputation. The authors propose to use a profile in a professional social network more widely in order to develop a specialist's career.

scholarly journals Measuring the value of the social network: analysis, quantitative approach, and application

2015 ◽  
Author(s):  
Sujata Jindal ◽  
Ritu Sindhu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Impact ◽  
Quantitative Approach ◽  
Social Data ◽  
The Social ◽  
The Impact ◽  
Day By Day

Social networks are growing day by day. Users of the social networks are generating values for these networks. All the users can’t be considered equal as they have different social network impact value. In this paper we analyze the social impact of a user and propose a method to estimate an individual’s worth to a social network in terms of impact. The mathematical evaluations show the effectiveness of our method. Based on the proposed method many applications can be built taking into consideration the impact any individual’s social profile has. We have tried to make various social data attributes more valuable and meaningful.

scholarly journals Can Social Networks Improve User Subjective Wellbeing?

2021 ◽  
Vol 29 (4) ◽  
pp. 53-77
Author(s):  
Md. Aftab Uddin ◽  
Monowar Mahmood ◽  
Alexandr Ostrovskiy ◽  
Ha Jin Hwang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sampling Method ◽  
Subjective Wellbeing ◽  
Asian Country ◽  
Negative Effects ◽  
Central Asian ◽  
Convenience Sampling ◽  
The Impact ◽  
The Relationship

Based on the tenets of the uses and gratifications theory (UGT) of media, this study investigates the impact of information gratifications on the subjective wellbeing of social network users in a central Asian country. Data from 244 adolescents were collected using a convenience sampling method. The study reveals the effect of information gratifications on subjective wellbeing, though this influence appears to be moderated by user habits in terms of passion and obsession toward social network use. Furthermore, personality traits have a significant moderating influence on the relationship between information gratifications and subjective wellbeing. Using the empirical findings, this study offers recommendations to mitigate the negative effects of social networks on users' subjective wellbeing.

scholarly journals SOCIAL NETWORKS, HEALTH, AND WELL-BEING AMONG PEOPLE LIVING WITH DEMENTIA

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S71-S71
Author(s):  
Eleanor S McConnell ◽  
Kirsten Corazzini ◽  
T Robert Konrad
Keyword(s):  
Older Adults ◽  
Social Networks ◽  
Well Being ◽  
Sensor Technology ◽  
Web Based ◽  
Use Of Technology ◽  
Care Partners ◽  
The Impact ◽  
The Relationship ◽  
Health And Well Being

Abstract Although the impact of dementia on the health and well-being of those living with Alzheimer’s Disease and related Disorders (ADRD) and their care partners has been widely studied, less attention has been paid to how the disease impacts individuals within the context of their larger social networks. This symposium presents findings from a series of integrated studies aimed at strengthening measurement of health and well-being among older adults with living with dementia and well-being among members of their social networks. Findings will be presented from five studies: (1) a scoping review of social network measurement in older adults in chronic illness, including dementia, that emphasizes the use of technology in measuring older adults’ social networks; (2) a simulation study to evaluate the feasibility and reliability of sensor technology to measure social interaction among a person living with dementia and others in their immediate surroundings; (3) development of a web-based application that allows older adults to map and activate their social networks; (4) a qualitative analysis of interviews from persons living with dementia, their unpaid caregivers, and paid caregivers from an adult day health program concerning well-being focused outcomes; and (5) a mixed methods analysis of the feasibility of using both traditional and novel measures of health and well-being deployed among networks of people living with dementia. Emerging technologies for measuring social networks health and well-being hold promise for advancing the study of the relationship-based nature of care for people living with dementia.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

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 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.

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