scholarly journals Learning Asynchronous-Time Information Diffusion Models and Its Application to Behavioral Data Analysis over Social Networks

2013 ◽  
Vol 1 (2) ◽  
pp. 30-57 ◽  
Author(s):  
Kazumi Saito ◽  
Masahiro Kimura ◽  
Kouzou Ohara ◽  
Hiroshi Motoda
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Information Diffusion ◽  
Diffusion Models ◽  
Behavioral Data ◽  
Time Information

A Survey on Information Diffusion Models in Social Networks

2018 ◽  
pp. 426-439 ◽  
Author(s):  
Shashank Sheshar Singh ◽  
Kuldeep Singh ◽  
Ajay Kumar ◽  
Harish Kumar Shakya ◽  
Bhaskar Biswas
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Diffusion Models

Information diffusion on communication networks based on Big Data analysis

2017 ◽  
Vol 35 (4) ◽  
pp. 745-757 ◽  
Author(s):  
Nan Zhou ◽  
Xiu-Xiu Zhan ◽  
Song Lin ◽  
Shang-Hui Yang ◽  
Chuang Liu ◽  
...  
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Communication Networks ◽  
Information Diffusion ◽  
Modern Society ◽  
Human Society ◽  
Content Type ◽  
Number Of Factors ◽  
The Future ◽  
Information Dynamic

Purpose Information carriers (including mass media and We-Media) play important roles in information diffusion on social networks. The purpose of this paper is to investigate changes in the dissemination of information combing with data analysis. Design/methodology/approach This work analyzed nearly 200 years of coverage of different information carriers during different periods of human society, from the period of only mouth-to-mouth communication to the period of modern society. Information diffusion models are built to illustrate how the information dynamic changes with time and combined box office data of several movies to predict the process of information diffusion. In addition, a metric is defined to identify which information would become news in the future. Findings Results show that with the development of information carriers, information spreads faster and wider nowadays. The correctness of the metric proposed has been validated. Research limitations/implications The structure of social networks influences the dissemination of information. There are an enormous number of factors that influence the formation of hotspots. Practical implications The results and conclusion of this work will benefit by predicting the evolution of information carriers. The metric proposed will aid in searching hot news in the future. Originality/value This work may shed some light on a better understanding of information diffusion, spreading not only on social networks but also on the carriers used for the information spreading.

Ricerca preliminare sulla valutazione Italiana dell'Internet Disorder Scale – Short Form (IDS9-SF)

2019 ◽  
Author(s):  
Paolo Soraci
Keyword(s):  
Social Networks ◽  
Factor Analysis ◽  
Data Analysis ◽  
Concurrent Validity ◽  
Short Form ◽  
Analysis Techniques ◽  
Validity Criterion ◽  
Preliminary Research ◽  
Nomological Validity ◽  
Italian Territory

The purpose of this study is to create a new tool capable of diagnosing the severity of internet addiction (IA) and is based on the nine IGD criteria. These same criteria were suggested by the APA in the last edition of the DSM-51. A sample was recruited with a method of convenience and 300+ participants were recruited through different forums and social networks. The construct validity of the IDS9SF test was achieved through factor analysis and nomological validity. The concurrent validity, criterion and reliability of the test itself have been thoroughly investigated through the most common and consolidated data analysis techniques, confirming that the same test has sufficient psychometric properties to be used also in the Italian territory. Furthermore it is necessary to remember that this preliminary research is only valid in the field of data and statistics with all the limitations of the case and cannot be used for a real clinical evaluation.

Architecture of a distributed system for processing heterogeneous data from social networks

2020 ◽  
Vol 4 ◽  
pp. 97-100
Author(s):  
A.P. Pronichev ◽  
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Distributed System ◽  
Heterogeneous Data ◽  
Analysis Process ◽  
Support Costs

The article discusses the architecture of a system for collecting and analyzing heterogeneous data from social networks. This architecture is a distributed system of subsystem modules, each of which is responsible for a separate task. The system also allows you to use external systems for data analysis, providing the necessary interface abstraction for connection. This allows for more flexible customization of the data analysis process and reduces development, implementation and support costs.

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