Research on influencing factors of information diffusion in online social networks under different themes

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ling Zhang ◽  
De Li ◽  
Robert J Boncella
Keyword(s):  
Social Network ◽  
Influencing Factors ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Online Social Network ◽  
Research Information ◽  
Political Event ◽  
Intelligent Support ◽  
Content Type ◽  
Factors Affecting

Purpose This paper aims to study the factors influencing online social network (OSN) information diffusion under different themes helps to understand information diffusion in general. Design/methodology/approach This study collects data from the Web of Science, use the strategic consulting intelligent support system for word frequency analysis and use keyword clustering to classify themes, then research information themes as influencing factors of OSN information diffusion. Findings Five themes of “natural disaster”, “political event”, “product marketing”, “sport and entertainment” and “health-disease” have been identified. It is found that the research objects, research methods and research theories used by scholars under different themes have different focuses, and the factors affecting information diffusion are different. Research limitations/implications The limitation of this paper is that it only focuses on five typical themes, and there may be more themes. Practical implications The research helps other scholars to conduct in-depth research on the diffusion of OSN information under different topics and focus on the content of the research on OSN information diffusion under different topics. Social implications The research helps other scholars to conduct in-depth research on the diffusion of social network information under different topics, so as to better understand and predict the law of information diffusion. Originality/value The research summarizes the research on information diffusion in OSNs from the theme level and analyses the key points and theories and further enriches the research system on information diffusion in OSNs.

scholarly journals QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4882 ◽  
Author(s):  
Fernando Terroso-Saenz ◽  
Andres Muñoz ◽  
José Cecilia
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Online Social Networks ◽  
Road Traffic ◽  
Online Social Network ◽  
Network Data ◽  
Regression Problem ◽  
Factors Affecting ◽  
Demand Prediction ◽  
Social Network Data

Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.

The effect of online social networks and competency-based management on innovation capability

2016 ◽  
Vol 20 (3) ◽  
pp. 499-511 ◽  
Author(s):  
Daniel Palacios-Marqués ◽  
Simona Popa ◽  
María Pilar Alguacil Mari
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Cognitive Processes ◽  
Online Social Networks ◽  
Online Social Network ◽  
Time Lags ◽  
Innovation Capability ◽  
Content Type ◽  
Competency Based ◽  
Telecommunications Industries

Purpose The purpose of this paper is to explore the effect of online social networks and competency-based management on innovation capability. Design/methodology/approach The paper is theory-confirming. Theoretical relationships were tested using an empirical study of 289 firms from the Spanish biotechnology and telecommunications industries. Findings Results confirm that online social network use for internal cognitive processes (e.g. reading, searching and storing information) and external cognitive processes (e.g. sharing and co-creating knowledge) positively affects knowledge transfer. This knowledge helps firms to achieve superior competency in R&D to succeed in innovation programs. Research Limitations/implications All survey respondents were from Spain, which may limit the generalizability of findings. A longitudinal approach was not used. However, doing so would make it possible to explore time lags between online social network use, competency-based management and innovation. Practical Implications This paper highlights the potential as well as the limitations of online social networks and competency-based management in promoting innovation capability. Businesses must consciously manage the assimilation and use of online social networks to benefit from them. Originality/value The study contributes to the literature by identifying effects on innovation capability at the meso-level (i.e. online social networks). Findings highlight the need for a shift in focus away from collaborating and interacting in online social networks (micro-level) and organizational contexts (macro-level) so as to improve innovation capability.

scholarly journals Influence Maximization Based on Backward Reasoning in Online Social Networks

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3189
Author(s):  
Lin Zhang ◽  
Kan Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Online Social Network ◽  
Rapid Development ◽  
Influence Maximization ◽  
Research Issues ◽  
Backward Reasoning ◽  
New Framework

Along with the rapid development of information technology, online social networks have become more and more popular, which has greatly changed the way of information diffusion. Influence maximization is one of the hot research issues in online social network analysis. It refers to mining the most influential top-K nodes from an online social network to maximize the final propagation of influence in the network. The existing studies have shown that the greedy algorithms can obtain a highly accurate result, but its calculation is time-consuming. Although heuristic algorithms can improve efficiency, it is at the expense of accuracy. To balance the contradiction between calculation accuracy and efficiency, we propose a new framework based on backward reasoning called Influence Maximization Based on Backward Reasoning. This new framework uses the maximum influence area in the network to reversely infer the most likely seed nodes, which is based on maximum likelihood estimation. The scheme we adopted demonstrates four strengths. First, it achieves a balance between the accuracy of the result and efficiency. Second, it defines the influence cardinality of the node based on the information diffusion process and the network topology structure, which guarantees the accuracy of the algorithm. Third, the calculation method based on message-passing greatly reduces the computational complexity. More importantly, we applied the proposed framework to different types of real online social network datasets and conducted a series of experiments with different specifications and settings to verify the advantages of the algorithm. The results of the experiments are very promising.

scholarly journals Investigating the Key Factors Influencing the Use of Online Social Networks in Public Sector Context in the UAE

2019 ◽  
Vol 7 (3) ◽  
pp. 392-411
Author(s):  
Ali Amin ◽  
Hamad Almari ◽  
Osama Isaac ◽  
Fathey Mohammed
Keyword(s):  
Social Network ◽  
Public Sector ◽  
Online Social Networks ◽  
Structural Equation ◽  
Online Social Network ◽  
Equation Modeling ◽  
Factors Affecting ◽  
Actual Usage ◽  
Use Of Technology ◽  
Public Sector Organizations

During the past decade, usage of online social network sites has grown dramatically rivaling search engines as the most visited Internet site. The intensive literature review reveals the existing of several studies that have been done on the field of Online Social Network (OSN), but there is a lack of research that deals with usage in the context of public sector organizations. This study aims to employ structural equation modeling via AMOS to analyze 401 valid questionnaires for assessing a model which is proposed based on Unified Theory of Acceptance and Use of Technology (UTAUT) to identify the factors affecting the use of OSN among employees of a public sector organization in the UAE. The proposed model examines the influence of four factors; performance, effort, social influence, and facilitating conditions on the actual use of OSN. Results indicated that all these factors (as independent variables) significantly predicted the actual usage of OSN with various percentages. Our work improved the insights on the online social networking usage in the context of public sector organizations.

scholarly journals Others' fortune in online vs offline settings: how envy affects people's intention to share information

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ana Suárez Vázquez ◽  
Manuel Chica Serrano
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Sharing ◽  
Online Social Networks ◽  
Online Social Network ◽  
Social Comparisons ◽  
Active Role ◽  
Content Type ◽  
The Individual ◽  
Share Information

PurposeThis paper aims to fill a gap in the existing literature by answering the following question: is the effect of envy on people's intention to share information the same in offline settings and on online social networks?Design/methodology/approachTwo studies demonstrate (1) how envy that results from upward social comparisons affects people's intention to share information and (2) the difference between online and offline settings.FindingsThe likelihood of sharing information susceptible of triggering envy is lower in online social networks than in an offline scenario.Research limitations/implicationsIn digital environments, feelings of envy depend on the number of social comparisons that the individual is exposed to.Practical implicationsThis research recommends (1) incorporating tools that allow online social network users to feel part of their network's successes, (2) promoting offline diffusion of information and (3) encouraging people to play an active role when using online social networks.Social implicationsBenefits can be derived from offering tools that permit receivers to take advantage of the selective self-presentation of other users. Such tools could have positive consequences for the welfare of online social network users.Originality/valueTo date, the literature has paid no attention to envy as an engine of information sharing. This aspect is especially relevant when discussing platforms whose main goal is precisely information sharing and that offer fertile ground for upward social comparisons.

A study of blog networks to determine online social network properties from the tie strength perspective

2014 ◽  
Vol 38 (3) ◽  
pp. 381-398 ◽  
Author(s):  
Terry Hui-Ye Chiu ◽  
Chien-Chou Chen ◽  
Yuh-Jzer Joung ◽  
Shymin Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Virtual Communities ◽  
Online Social Network ◽  
Strong Association ◽  
Tie Strength ◽  
Content Type ◽  
Network Properties ◽  
Strength Perspective

Purpose – Most studies on tie strength have focused on its definition, calculation and applications, but have not paid much attention to how tie strength can help analyse online social networks. Because ties play different roles in a network depending on their strength, the purpose of this paper is to explore the relationship between tie strength and network behaviours. Design/methodology/approach – The authors propose a simple metric for tie strength measurement and then apply it to an online social network extracted from a blog network. These networks are massive in size and have technology for efficient data collection, thereby presenting the possibility of measuring tie strength objectively. From the results several key social network properties are studied to see how tie strength may be used as a metric to explain certain characteristics in social networks. Findings – The online networks exhibit all the structural properties of an actual social network, not only in following the power law but also with regard to the distribution of tie strength. The authors noted a strong association between tie strength and reciprocity, and tie strength and transitivity in online social networks. Originality/value – This paper highlights the importance of analysing online social networks from a tie strength perspective. The results have important implications for the development of efficient search mechanisms and appropriate group leaders in virtual communities.

scholarly journals How migrants manifest their transnational identity through online social networks: comparative findings from a case of Koreans in Germany

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Sunyoung Park ◽  
Lasse Gerrits
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Point Of View ◽  
Comparative Case Study ◽  
Transnational Identity ◽  
Transnational Identities ◽  
Web 3.0 ◽  
Situational Contexts

AbstractAlthough migration has long been an imperative topic in social sciences, there are still needs of study on migrants’ unique and dynamic transnational identity, which heavily influences the social integration in the host society. In Online Social Network (OSN), where the contemporary migrants actively communicate and share their stories the most, different challenges against migrants’ belonging and identity and how they cope or reconcile may evidently exist. This paper aims to scrutinise how migrants are manifesting their belonging and identity via different technological types of online social networks, to understand the relations between online social networks and migrants’ multi-faceted transnational identity. The research introduces a comparative case study on an online social movement led by Koreans in Germany via their online communities, triggered by a German TV advertisement considered as stereotyping East Asians given by white supremacy’s point of view. Starting with virtual ethnography on three OSNs representing each of internet generations (Web 1.0 ~ Web 3.0), two-step Qualitative Data Analysis is carried out to examine how Korean migrants manifest their belonging and identity via their views on “who we are” and “who are others”. The analysis reveals how Korean migrants’ transnational identities differ by their expectation on the audience and the members in each online social network, which indicates that the distinctive features of the online platform may encourage or discourage them in shaping transnational identity as a group identity. The paper concludes with the two main emphases: first, current OSNs comprising different generational technologies play a significant role in understanding the migrants’ dynamic social values, and particularly, transnational identities. Second, the dynamics of migrants’ transnational identity engages diverse social and situational contexts. (keywords: transnational identity, migrants’ online social networks, stereotyping migrants, technological evolution of online social network).

Efficient Time Series Clustering and Its Application to Social Network Mining

2014 ◽  
Vol 23 (2) ◽  
pp. 213-229 ◽  
Author(s):  
Cangqi Zhou ◽  
Qianchuan Zhao
Keyword(s):  
Time Series ◽  
Social Network ◽  
Information Diffusion ◽  
Time Series Data ◽  
Online Social Network ◽  
Dissimilarity Measure ◽  
Series Data ◽  
Formation Mechanisms ◽  
Network Analyses ◽  
Definition Of

AbstractMining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

Personality, risk tolerance and social network use: an exploratory study

2016 ◽  
Vol 42 (6) ◽  
pp. 536-552 ◽  
Author(s):  
Shaista Wasiuzzaman ◽  
Siavash Edalat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Financial Risk ◽  
Ordinary Least Squares ◽  
Risk Tolerance ◽  
Network Activity ◽  
Least Squares Regression ◽  
Content Type ◽  
Financial Risk Tolerance

Purpose – The vast amount of information available via online social networks (OSN) makes it a very good avenue for understanding human behavior. One of the human characteristics of interest to financial practitioners is an individual’s financial risk tolerance. The purpose of this paper is to look at the relationship between an individual’s OSN behavior and his/her financial risk tolerance. Design/methodology/approach – The study uses data collected from a sample of 220 university students and the backward variables selection ordinary least squares regression analysis technique to achieve its objective. Findings – The results of the study find that the frequency of logging on to social network sites indicates an individual who has higher financial risk tolerance. Additionally, the increasing use of social networks for social connection is found to be associated with lower financial risk tolerance. The results are mostly consistent when the sample is split based on prior financial knowledge. Originality/value – To the authors’ knowledge this is the first study which documents the possibility of understanding an individual’s financial risk tolerance via his/her social network activity. This provides investment/financial consultants with more avenues for gathering information in order to understand their current or potential clients hence providing better services.

Quantifying Virality of Information in Online Social Networks

2012 ◽  
Vol 4 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Abhishek Vaish ◽  
Rajiv Krishna G. ◽  
Akshay Saxena ◽  
Dharmaprakash M. ◽  
Utkarsh Goel
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Critical Factors ◽  
Asset Valuation ◽  
Valuation Technique ◽  
Information Item ◽  
Viral Nature ◽  
Over Time

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. They use a popularly available YouTube dataset to collect attributes and measure critical factors such as share-count, appreciation, user rating, controversiality, and comment rate. These variables are used with a proposed formula to obtain viral index of each video on a given date. The authors then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results.The research demonstrates the dependency of virality on critical social network factors. With the help of a second dataset acquired, the authors determine the pattern virality of an information item takes over time.

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