scholarly journals Coworking spaces and virtual learning communities in Social Networks: Case Study of #ElClaustroDeIG on Instagram

Aula Abierta ◽  
2021 ◽  
Vol 50 (1) ◽  
pp. 453-464
Author(s):  
Raquel Gómez-Martínez ◽  
Luis M. Romero-Rodríguez
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Learning Communities ◽  
Preschool Education ◽  
Virtual Learning ◽  
Ideal Space ◽  
Good Practices ◽  
Patterns Of Use ◽  
The Impact

Social networks are consolidated as spaces for the exchange of valuable content. It is not surprising to find spaces for the exchange of good teaching practices in virtual learning communities, so the objective of this study is to analyze, through the case study of #ElClaustrodeIG on Instagram, the educational content focused on teaching that is shared for the Ibero-American community in Spanish, as well as the analysis of the users who share their experiences and good practices in this social network. In order to do this, content analysis is first carried out, through an analysis sheet to 300 posts and a questionnaire is applied to 130 users of this hashtag on Instagram in order to analyze their patterns of use, their interests and motivations, their training in ICT and social networks, as well as the impact that the use of this social network has on their professional development and the gratification they expect. The results show that most of the publications are about good practices for primary and preschool education while most of the users think that Instagram is an ideal space for non-formal learning, applying in the classrooms many of the good practices shared in this coworking space. 

MapTap and Cornelia Slow Digital Art History and Formal Art Historical Social Network Research

2016 ◽  
Vol 79 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Koenraad Brosens ◽  
Klara Alen ◽  
Astrid Slegten ◽  
Fred Truyen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Early Modern ◽  
Data Model ◽  
Art History ◽  
Digital Art ◽  
Unique Data ◽  
Network Research ◽  
Custom Made

Abstract The essay introduces MapTap, a research project that zooms in on the ever-changing social networks underpinning Flemish tapestry (1620 – 1720). MapTap develops the young and still slightly amorphous field of Formal Art Historical Social Network Research (FAHSNR) and is fueled by Cornelia, a custom-made database. Cornelia’s unique data model allows researchers to organize attribution and relational data from a wide array of sources in such a way that the complex multiplex and multimode networks emerging from the data can be transformed into partial unimode networks that enable proper FAHSNR. A case study revealing the key roles played by women in the tapestry landscape shows how this kind of slow digital art history can further our understanding of early modern creative communities and industries.

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 LIFE ON DISPLAY: WHY IT IS IMPORTANT TO EXPOSE YOUR ACTIONS ON THE SOCIAL NETWORK

2021 ◽  
Vol 6(72) (4) ◽  
pp. 41-48
Author(s):  
A. E. Starchenko ◽  
M. V. Semina
Keyword(s):  
Higher Education ◽  
Social Networks ◽  
Social Network ◽  
Reference Group ◽  
Human Life ◽  
Sociological Study ◽  
The Social ◽  
Personal Accounts ◽  
The Impact ◽  
To Receive

Social networks have emerged relatively recently in human life, but have already become an integral part of it. Companies tell about themselves, their activities, innovations, promotions and events in their profiles. This helps increase audience coverage, tell more about your brand, products, services. People in personal accounts have the opportunity to share their lives and creativity through photos, videos and texts. Now it is not necessary to receive higher education to become an operator, director or actor whose talent is recognized by society. It is enough to start a page on the social network and start sharing your knowledge and creativity. To find out why people post photos, videos and write texts on their social networks, a pilot sociological study was carried out. The method of deep interview with active users of social networks was chosen to carry out the study. The interview allowed getting unique information, to learn the opinion of users about social networks, the impact of the new way of communication on their life, to identify the reasons why users start and maintain profiles. The respondents were 20 users of social networks between the ages of 19 and 22. Interviewees have profiles on the most popular Instagram and Vkontakte networks. As a result of the analysis of the interview, a tendency was revealed to differ in the perception of users of their actions on the social network and similar actions of other users. Their content is perceived by them as opportunities to be in sight, as a resource to form their social status and an element of influence on their reference group. And the same content published by others is perceived as boasting.

The Impact of Social Networks on Science Students' Academic Performance in Ondo State Senior Secondary Schools, Nigeria

2021 ◽  
pp. 50-63
Author(s):  
Jethro Oludare OLOJO
Keyword(s):  
Social Networks ◽  
Academic Performance ◽  
Social Network ◽  
Science Students ◽  
The Social ◽  
Ondo State ◽  
Research Questions ◽  
Senior Secondary Schools ◽  
Senior Secondary ◽  
The Impact

The objective of this study was to examine the impact of social network usage on science students’ academic achievements in Ondo State’s senior secondary schools. The study was also to find the extent to which students under investigation used the social network platforms and the frequencies of their visits. In order to achieve this, a structured questionnaire was designed and administered to students from the three senatorial districts that made up the state. A multistage; which involved simple random and purposive sampling approaches was used to select the sample for the study. 150 copies of the questionnaire were distributed; out of which, 148 (98.78%) copies were returned. For the study, four research questions and two research hypotheses were developed. The hypotheses were assessed using the student's - t statistic at 0.05 significant level; using SPSS version 20 while the research questions formulated were evaluated using frequency counts and percentages. The study revealed that Ondo State senior secondary school science students can efficiently use the social network platforms for academic activities with male students being more proficient than their female counterparts. The study also revealed that the usage of social networks has assisted students to improve their academic performance; irrespective of their classes. Besides, the study showed that Facebook was the most popular of all the social network platforms. To this end, the researcher recommended that teachers, parents, and guidance should monitor the activities of their wards on the social network sites so that they can use the platforms to benefit their lots. Teachers should also use the advantage of students’ exposure to social networking to change their teaching methods from traditional one to online teaching.

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