Fuzzy-Based Techniques in Human-Like Processing of Social Network Data

2015 ◽  
Vol 23 (Suppl. 1) ◽  
pp. 1-14 ◽  
Author(s):  
Lotfi A. Zadeh ◽  
Ali M. Abbasov ◽  
Shahnaz N. Shahbazova
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Fuzzy Sets ◽  
Possibility Theory ◽  
Linguistic Terms ◽  
Social Network Data ◽  
Processing Data ◽  
Source Of Information ◽  
Analysis Of Social Networks ◽  
Theory Of Fuzzy Sets

Social networks have gained a lot attention. They are perceived as a vast source of information about their users. Variety of different methods and techniques has been proposed to analyze these networks in order to extract valuable information about the users – things they do and like/dislike. A lot of effort is put into improvement of analytical methods in order to grasp a more accurate and detailed image of users. Such information would have an impact on many aspects of everyday life of people – from politics, via professional life, to shopping and entertainment. The theory of fuzzy sets and systems, introduced in 1965, has the ability to handle imprecise and ambiguous information, and to cope with linguistic terms. The theory has evolved into such areas like possibility theory and computing with words. It is very suitable for processing data in a human-like way, and providing the results in a human-oriented manner. The paper presents a short survey of works that use fuzzy-based technologies for analysis of social networks. We pose an idea that fuzzy-based techniques allow for introduction of humancentric and human-like data analysis processes. We include here detailed descriptions of a few target areas of social network analysis that could benefit from applications of fuzzy sets and systems methods.

11. A Liang-Zeger Method for Modeling Dyadic Interdependence in the Analysis of Social Networks

2003 ◽  
Vol 33 (1) ◽  
pp. 343-380 ◽  
Author(s):  
Kazuo Yamaguchi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Covariance Matrix ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Network Data ◽  
Social Network Data ◽  
Analysis Of Social Networks ◽  
Illustrative Application ◽  
Variance Covariance Matrix

This article introduces a modified Liang-Zeger method for the estimation of the variance-covariance matrix of parameter estimates for models of social network data that include variables to characterize dyadic nonindependence. While the pseudolikelihood method has been used recently to estimate parameters for such models, the issue of estimating their standard errors, or the variance-covariance matrix more generally, has been neglected. This article addresses the issue by proposing a method for such estimation and also presents an illustrative application of the method to empirical social network data.

scholarly journals Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

2012 ◽  
Vol 367 (1599) ◽  
pp. 2108-2118 ◽  
Author(s):  
Louise Barrett ◽  
S. Peter Henzi ◽  
David Lusseau
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
The Other ◽  
Feedback Process ◽  
Cognitive Evolution ◽  
Systems Approaches ◽  
Entropy Measures ◽  
Free Ranging ◽  
Source Of Information

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.

Social Networks and Crime: Pitfalls and Promises for Advancing the Field

2019 ◽  
Vol 2 (1) ◽  
pp. 99-122 ◽  
Author(s):  
Katherine Faust ◽  
George E. Tita
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Potential Measurement ◽  
Fully Integrated ◽  
Social Network Data ◽  
The Past ◽  
Dynamic Social Network ◽  
Criminological Research ◽  
The Social ◽  
Criminological Theories

Over the past decade, a considerable literature has emerged within criminology stemming from the collection of social network data and the adoption of social network analysis by a cadre of scholars. We review recent contributions to four areas of crime research: co-offending networks, illicit networks, gang-rivalry networks, and neighborhoods and crime. Our review highlights potential pitfalls that one might encounter when using social networks in criminological research and points to fruitful directions for further research. In particular, we recommend paying special attention to the clear specifications of what ties in the network are assumed to be doing, potential measurement weaknesses that can arise when using police or investigative data to construct a network, and understanding dynamic social network processes related to criminological outcomes. We envision a bright future in which the social network perspective will be more fully integrated into criminological theories, analyses, and applications.

Genetic Algorithm Applied in the Social Network Data Analysis

2013 ◽  
Vol 427-429 ◽  
pp. 2188-2191
Author(s):  
Lei Liu ◽  
Quan Bao Gao
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Data Analysis ◽  
Rapid Development ◽  
Virtual Community ◽  
Social Network Data ◽  
Starting Point ◽  
The Social ◽  
Optimal Data Analysis

The rapid development of network and information technology makes the network become the indispensable part in people's life. Network design uses email as a starting point, instead of actual letters. Then Happy Nets, BBS etc. are evolved from it, with virtual as their major feature. In the process of social networks evolution, the personal image transformed from the actual into the virtual one. All this has contributed to the birth of the social network, which then makes the contacts among people presenting the feature of network expansion and cost reduction. The popular social network nowadays is considered to be social plus network, namely, through the network, as a carrier, people are connected to form a virtual community with certain characteristics. Based on the genetic algorithm and genetic coding technology, the article is designed to make the optimal data analysis and create a optimistic cyber environment in the process of the social networks explosive development.

Classifying the Influential Individuals in Multi-Layer Social Networks

2019 ◽  
Vol 8 (1) ◽  
pp. 21-32
Author(s):  
Ruchi Mittal ◽  
M.P.S Bhatia
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Real Life ◽  
Single Layer ◽  
Machine Learning Techniques ◽  
Centrality Measures ◽  
Learning Techniques ◽  
Modes Of Interaction ◽  
Source Of Information

Nowadays, social media is one of the popular modes of interaction and information diffusion. It is commonly found that the main source of information diffusion is done by some entities and such entities are also called as influencers. An influencer is an entity or individual who has the ability to influence others because of his/her relationship or connection with his/her audience. In this article, we propose a methodology to classify influencers from multi-layer social networks. A multi-layer social network is the same as a single layer social network depict that it includes multiple properties of a node and modeled them into multiple layers. The proposed methodology is a fusion of machine learning techniques (SVM, neural networks and so on) with centrality measures. We demonstrate the proposed algorithm on some real-life networks to validate the effectiveness of the approach in multi-layer systems.

scholarly journals Image Based Classification of Rumor Information from the Social Network Platform

2021 ◽  
Vol 38 (5) ◽  
pp. 1413-1421
Author(s):  
Vallamchetty Sreenivasulu ◽  
Mohammed Abdul Wajeed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
End Users ◽  
Web Based ◽  
Suggested Approach ◽  
Text Input ◽  
Social Network Data ◽  
Proposed Model ◽  
Spam Filters ◽  
Network Platform

Spam emails based on images readily evade text-based spam email filters. More and more spammers are adopting the technology. The essence of email is necessary in order to recognize image content. Web-based social networking is a method of communication between the information owner and end users for online exchanges that use social network data in the form of images and text. Nowadays, information is passed on to users in shorter time using social networks, and the spread of fraudulent material on social networks has become a major issue. It is critical to assess and decide which features the filters require to combat spammers. Spammers also insert text into photographs, causing text filters to fail. The detection of visual garbage material has become a hotspot study on spam filters on the Internet. The suggested approach includes a supplementary detection engine that uses visuals as well as text input. This paper proposed a system for the assessment of information, the detection of information on fraud-based mails and the avoidance of distribution to end users for the purpose of enhancing data protection and preventing safety problems. The proposed model utilizes Machine Learning and Convolutional Neural Network (CNN) methods to recognize and prevent fraud information being transmitted to end users.

scholarly journals Press X Governement

2021 ◽  
Vol 17 (3) ◽  
pp. 562-595
Author(s):  
Carla Montuori Fernandes ◽  
Luiz Ademir de Oliveira ◽  
Mayra Regina Coimbra ◽  
Mariane Motta de Campos
Keyword(s):  
Social Networks ◽  
Content Analysis ◽  
Social Network ◽  
Support Network ◽  
Redes Sociales ◽  
Health Crisis ◽  
The Social ◽  
The Press ◽  
La Red ◽  
Analysis Of Social Networks

ABSTRACT – This paper begins with a discussion of the concept of populism in order to analyze how Jair Bolsonaro’s criticisms of the press circulated on the social network Twitter at a time when Brazil had recorded the highest number of covid-related deaths, in the first week of March 2021. This paper presupposes that the president’s support network incorporated the populist binary rhetoric of “us” against a “corrupt elite” which is responsible for conspiring and amplifying the effects of the health crisis in the country. As a methodology, we opted for a mixed proposal based on content analysis and analysis of social networks. As a result, we found that the tweets from Bolsonaro supporters claim that the press is corrupt, and manipulates and harasses the president in its coverage of the pandemic. RESUMO – O artigo parte da discussão do conceito de populismo, com o objetivo de analisar como as críticas de Jair Bolsonaro à imprensa circularam na rede social Twitter no momento em que o Brasil atingia o maior número de mortos pela covid-19, na primeira semana de março de 2021. O texto traz como hipótese que a rede de apoio ao presidente incorporou a retórica binarista do “nós” contra uma “elite corrupta” que é responsável por conspirar e ampliar os efeitos da crise sanitária no país. Como metodologia, optou-se por uma proposta mista ancorada na análise de redes sociais e análise de conteúdo. Como resultado, constatou-se que os tweets dos apoiadores de Bolsonaro associaram a imprensa atributos de corrupção, manipulação e perseguição ao líder na cobertura contra uma “elite corrupta” e atribuiu a imprensa o caráter dos efeitos da pandemia. RESUMEN - Artículo de la discusión del concepto de populismo, con el fin de analizar cómo circularon en la red social Twitter el comunicado de prensa de Jair Bolsonaro al equipo cuando Brasil alcanzó el mayor número de muertes por covid-19, en la primera semana de marzo de 2021. El texto plantea la hipótesis de que la red de apoyo al presidente incorporó la retórica del binarismo populista del “nosotros” frente a una “élite corrupta” y atribuyó a la prensa el carácter de enemigo del gobierno, responsable de conspirar y amplificar los efectos de la salud en el pais. Como metodología, optamos por una propuesta mixta anclada en el análisis de redes sociales y análisis de contenido. Como resultado, se encontró que los tuits de simpatizantes de Bolsonaro asociaron a la prensa con atributos de corrupción, manipulación y acoso al líder para cubrir los efectos de la pandemia.

scholarly journals Questions and features of automated social networks monitoring with support of intelligent user messages analysis.

2014 ◽  
Vol 2014 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Александр Подвесовский ◽  
Aleksandr Podvesovskiy ◽  
Дмитрий Будыльский ◽  
Dmitriy Budylskiy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Software Implementation ◽  
Network Data ◽  
Research Directions ◽  
Social Network Data ◽  
Monitoring Model

An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.

Network Basics: Points, Lines, and Positions

2020 ◽  
pp. 15-33
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Ethical Issues ◽  
Building Blocks ◽  
Levels Of Analysis ◽  
Network Data ◽  
Social Network Data ◽  
Basic Concepts ◽  
Statistical Approaches

This chapter presents an introduction to the basic concepts central to social network analysis. Written for those with little experience in the approach, the chapter aims to provide the necessary tools to dig deeper into exploring social networks via the subsequent chapters in this volume. It begins by introducing the building blocks of networks—nodes and edges—and their characteristics. Next, it outlines several of the major dimensions of network analysis, including the implications of boundary specification and levels of analysis. It also briefly introduces statistical approaches to networks and network data collection. The chapter concludes with a discussion of ethical issues that arise when collecting and analyzing social network data.

scholarly journals Metrics for Personal Profiles of Social Network Users

2020 ◽  
Vol 79 ◽  
pp. 01012
Author(s):  
Konstantin Sergeevich Nikolaev ◽  
Fail Mubarakovich Gafarov ◽  
Pavel Nikolaevich Ustin
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Web Application ◽  
Machine Learning Methods ◽  
Processing Data ◽  
Behavioral Predictors ◽  
Technical Details ◽  
Genre Preferences ◽  
Selection Of

This paper discusses the technical details of obtaining and processing data to determine a set of characteristics of texts from social networks, genre preferences in movies and music genres for students of Kazan Federal University who have different academic performance (successful, average, not-successful). The selection of such characteristics is carried out using machine learning methods (Word2Vec, tSNE). The data obtained is used in the development of a functional psychometric model of cognitive behavioral predictors of an individual’s activity within the framework of their educational activities. We also developed a web application for visualizing the obtained data using the Flask engine.

Sign in / Sign up

Export Citation Format

Share Document