scholarly journals An integrated model for discovering, classifying and labeling topics based on topic modeling

2014 ◽  
Vol 17 (2) ◽  
pp. 73-85
Author(s):  
Thanh Ho ◽  
Phuc Do
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Data Structure ◽  
Network Analysis ◽  
Topic Modeling ◽  
Integrated Model ◽  
Training Data ◽  
Analysis System

In this paper, we propose an integrated model for discovering, classifying and labeling topics of messages based on topic modeling to analyze and understand the topics of the messages posted by users on social networks. In which, the method of labeling is executed by machine learning on the training data and ontology. The ontology is created in the field of higher education. All parts of model are integrated on a system called social network analysis system based on topic modeling. The experiment of the model on the linguistic data of Vietnamese texts collected from a student forum is transformed into a data structure of social network, including: 13,208 messages by 2,494 users.

Philanthropic Giving by Foundations to Higher Education Institutions: A State-Level Social Network Analysis

2017 ◽  
Vol 1 (1) ◽  
pp. 1 ◽  
Author(s):  
Kevin R. McClure ◽  
Leah Frierson ◽  
Adam W. Hall ◽  
Kara L. Ostlund
Keyword(s):  
Higher Education ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Higher Education Institutions ◽  
State Level

Introduction

2020 ◽  
pp. xxii-13
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Bibliometric Analysis ◽  
Social Life ◽  
New Directions ◽  
Central Paradigm

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

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.

scholarly journals Social Network Analysis Visualization

2015 ◽  
pp. 257-270
Author(s):  
Mohana Shanmugam ◽  
Yusmadi Yah Jusoh ◽  
Rozi Nor Haizan Nor ◽  
Marzanah A. Jabar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Network ◽  
Future Research ◽  
Research Gaps ◽  
The Social ◽  
Network Studies ◽  
Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

scholarly journals Ego-Centric Social Network Analysis in Marketing of Fish From Cage Culture System In Simalungun District, Indonesia

2019 ◽  
Vol 8 (4) ◽  
pp. 105-113
Author(s):  
Feriel Amelia Sembiring ◽  
Fikarwin Zuska ◽  
Bengkel Ginting ◽  
Rizabuana Ismail ◽  
Henry Sitorus
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Culture System ◽  
Qualitative Approach ◽  
Mutual Trust ◽  
Cage Culture ◽  
The Social ◽  
The Village ◽  
The Relationship

Aquaculture of Cage Culture is one of the main activities carried out by the community in the village of Haranggaol to fulfill their economic needs. This cultivation business establishes a relationship between traders and cages in terms of marketing their crops. There are 3 egocentric actors in the Haranggaol area. They are collectors (entrepreneurs/farmers who own capital), namely the Rohakinian group, the Siharo group, and the Paimaham group. Through these three egocentric actors, a social network is formed with several alters. Based on the qualitative approach with use Ucinet software, the mapping of their social networks can be seen as follows: alter actors connected to the Rohakinian group are 12 farmers in the group and 2 farmers outside the group with a density of 0.033. There are 27 alter actors connected to the Siharo group, 21 from the group and 6 from outside the group with a density of 0.014. There are 27 alter actors connected to the Paimaham group, namely 36 farmers from their groups and 10 farmers outside the group with a density of 0.005. The social networks that occur between these actors are intertwined due to the existence of kinship relationships, family or close friends who know each other among them. The relationship between family, family or close friends built with mutual trust make this network integrated.

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.

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