Influence and Information Flow in Online Social Networks

2017 ◽  
Vol 9 (4) ◽  
pp. 1-17
Author(s):  
Afrand Agah ◽  
Mehran Asadi
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Data ◽  
Marketing Strategies ◽  
Large Data Sets ◽  
Divide And Conquer ◽  
Data Sets ◽  
Divide And Conquer Algorithm ◽  
Flow Of Information

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

Influence and Information Flow in Online Social Networks

2021 ◽  
pp. 502-520
Author(s):  
Afrand Agah ◽  
Mehran Asadi
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Online Social Networks ◽  
Large Data ◽  
Marketing Strategies ◽  
Divide And Conquer ◽  
Data Sets ◽  
Divide And Conquer Algorithm ◽  
Flow Of Information

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

scholarly journals A meritocratic network formation model for the rise of social media influencers

2021 ◽  
Author(s):  
Nicolò Pagan ◽  
Wenjun Mei ◽  
Cheng Li ◽  
Florian Dörfler
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Network Formation ◽  
Large Data ◽  
Network Size ◽  
Large Data Sets ◽  
Data Sets ◽  
Strategic Incentives ◽  
Quality Ranking ◽  
Social Network Formation

Abstract Many of today’s most used online social networks such as Instagram, Youtube, Twitter, or Twitch are based on User-Generated Content (UGC), and the exploration of this content is enhanced by the integrated search engines. Prior multidisciplinary effort on studying social network formation processes has privileged topological elements or socio-strategic incentives. Here, we propose an untouched meritocratic approach inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We statistically and numerically analyze the network equilibria properties: while the expected outdegree of the nodes remains bounded by the logarithm of the network size, the expected indegree follows a Zipf’s law with respect to the quality ranking. Notably, our quality-based mechanism provides an intuitive explanation of the origin of the Zipf’s regularity in growing social networks. Our theoretical results are empirically validated against large data-sets collected from Twitch, a fast-growing platform for online gamers.

scholarly journals A meritocratic network formation model for the rise of social media influencers

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nicolò Pagan ◽  
Wenjun Mei ◽  
Cheng Li ◽  
Florian Dörfler
Keyword(s):  
Online Social Networks ◽  
Network Formation ◽  
Preferential Attachment ◽  
Scaling Law ◽  
Small World ◽  
Large Data ◽  
Large Data Sets ◽  
Directed Network ◽  
Data Sets ◽  
Quality Ranking

AbstractMany of today’s most used online social networks such as Instagram, YouTube, Twitter, or Twitch are based on User-Generated Content (UGC). Thanks to the integrated search engines, users of these platforms can discover and follow their peers based on the UGC and its quality. Here, we propose an untouched meritocratic approach for directed network formation, inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We theoretically and numerically analyze the network equilibria properties under different meeting probabilities: while featuring common real-world networks properties, e.g., scaling law or small-world effect, our model predicts that the expected in-degree follows a Zipf’s law with respect to the quality ranking. Notably, the results are robust against the effect of recommendation systems mimicked through preferential attachment based meeting approaches. Our theoretical results are empirically validated against large data sets collected from Twitch, a fast-growing platform for online gamers.

scholarly journals ON PRIVACY CLASSIFICATION IN UBIQUITOUS COMPUTING SYSTEMS

2014 ◽  
pp. 26-35
Author(s):  
Dan Cvrcek ◽  
Vaclav Matyas ◽  
Marek Kumpost
Keyword(s):  
Ubiquitous Computing ◽  
Formal Model ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Computing Systems ◽  
Common Criteria ◽  
Trust Models ◽  
Ubiquitous Computing Environments

Many papers and articles attempt to define or even quantify privacy, typically with a major focus on anonymity. A related research exercise in the area of evidence-based trust models for ubiquitous computing environments has given us an impulse to take a closer look at the definition(s) of privacy in the Common Criteria, which we then transcribed in a bit more formal manner. This led us to a further review of unlinkability, and revision of another semi-formal model allowing for expression of anonymity and unlinkability – the Freiburg Privacy Diamond. We propose new means of describing (obviously only observable) characteristics of a system to reflect the role of contexts for profiling – and linking – users with actions in a system. We believe this approach should allow for evaluating privacy in large data sets.

scholarly journals Big Data System for Medical Images Analysis

2020 ◽  
Author(s):  
Janusz Bobulski ◽  
Mariusz Kubanek
Keyword(s):  
Big Data ◽  
Medical Images ◽  
Processing System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Computer Data ◽  
Fast Processing

Big Data in medicine includes possibly fast processing of large data sets, both current and historical in purpose supporting the diagnosis and therapy of patients' diseases. Support systems for these activities may include pre-programmed rules based on data obtained from the interview medical and automatic analysis of test results diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a Big Data computer data processing system using artificial intelligence to analyze and process medical images.

scholarly journals Data Mining in Tourism Data Analysis: Inbound Visitors to Japan

2017 ◽  
Vol 57 (3) ◽  
pp. 310-323 ◽  
Author(s):  
Valeriya Shapoval ◽  
Morgan C. Wang ◽  
Tadayuki Hara ◽  
Hideo Shioya
Keyword(s):  
Data Mining ◽  
Hot Springs ◽  
Large Data ◽  
Marketing Strategies ◽  
Large Data Sets ◽  
Destination Marketing ◽  
Data Sets ◽  
Future Return ◽  
Mining Tool ◽  
Natural Settings

The increasing power of technology puts new, advanced statistical tools at the disposal of researchers. This is one of the first research articles to use a data mining tool—namely, decision trees—to analyze the behavior of inbound tourists for the purpose of effective future destination marketing in Japan. The research results of approximately 4,000 observations show that the main motivation for visitors’ future return is not driven by experiences had during their most current visit but rather by experiences anticipated in the future, such as visiting hot springs or immersing themselves in beautiful natural settings. The data mining method largely excludes the possibility of the intrusion of researcher subjectivity and is conducive to useful discoveries of certain visitor patterns in large data sets, providing governments and destination marketing organizations with additional tools to better formulate effective destination marketing strategies.

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