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 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).

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.

scholarly journals Defining Inspection Techniques for Detecting Privacy Problems in Online Social Networks

2019 ◽  
Vol 10 ◽  
pp. 35
Author(s):  
Andrey  Rodrigues ◽  
Natasha  M. C. Valentim ◽  
Eduardo  Feitosa
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Empirical Study ◽  
Qualitative Analysis ◽  
Online Social Networks ◽  
Online Social Network ◽  
Effective Alternative ◽  
Daily Lives ◽  
Study Participants ◽  
Inspection Techniques

In the last few years, Online Social Networks (OSN) have experienced growth in the number of users, becoming an increasingly embedded part of people’s daily lives. Privacy expectations of OSNs are higher as more members start realizing potential privacy problems they face by interacting with these systems. Inspection methods can be an effective alternative for addressing privacy problems because they detect possible defects that could be causing the system to behave in an undesirable way. Therefore, we proposed a set of privacy inspection techniques called PIT-OSN (Privacy Inspection Techniques for Online Social Network). This paper presents the description and evolution of PIT-OSN through the results of a preliminary empirical study. We discuss the quantitative and qualitative results and their impact on improving the techniques. Results indicate that our techniques assist non-expert inspectors uncover privacy problems effectively, and are considered easy to use and useful by the study participants. Finally, the qualitative analysis helped us improve some technique steps that might be unclear.

A Novel Spatio-Temporal Access Control Model for Online Social Networks and Visual Verification

2021 ◽  
Vol 11 (2) ◽  
pp. 17-31
Author(s):  
Lanfang Zhang ◽  
Zhiyong Zhang ◽  
Ting Zhao
Keyword(s):  
Social Network ◽  
Access Control ◽  
Online Social Networks ◽  
Online Social Network ◽  
Rapid Development ◽  
Real Life ◽  
Control Model ◽  
Mobile Internet ◽  
Access Control Model ◽  
Spatio Temporal

With the rapid development of mobile internet, a large number of online social networking platforms and tools have been widely applied. As a classic method for protecting the privacy and information security of social users, access control technology is evolving with the spatio-temporal change of social application requirements and scenarios. However, nowadays there is a lack of effective theoretical model of social spatio-temporal access control as a guide. This paper proposed a novel spatio-temporal access control model for online social network (STAC) and its visual verification, combined with the advantages of discretionary access control, using formal language to describe the access control rules based on spatio-temporal, and real-life scenarios for access control policy description, realizes a more fine-grained access control mechanism for social network. By using the access control verification tool ACPT developed by NIST to visually verify the proposed model, the security and effectiveness of the STAC model are proved.

Building Online Social Networks to Engage Female Students in Information Systems

2015 ◽  
Vol 10 (4) ◽  
pp. 33-51
Author(s):  
Jaymeen R. Shah ◽  
Hsun-Ming Lee
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Systems ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Role Models ◽  
Online Social Network ◽  
Research Process ◽  
Female Students ◽  
The Social

During the next decade, enrollment growth in Information Systems (IS) related majors is unlikely to meet the predicted demand for qualified IS graduates. Gender imbalance in the IS related program makes the situation worse as enrollment and retention of women in the IS major has been proportionately low compared to male. In recent years, majority of high school and college students have integrated social networking sites in their daily life and habitually use these sites. Providing female students access to role models via an online social network may enhance their motivation to continue as an IS major and pursue a career in IS field. For this study, the authors follow the action research process – exploration of information systems development. In particular, a Facebook application was developed to build the social network connecting role models and students. Using the application, a basic framework is tested based on the gender of participants. The results suggest that it is necessary to have adequate number of role models accessible to students as female role-models tend to select fewer students to develop relationships with a preference for female students. Female students likely prefer composite role models from a variety of sources. This pilot study yields valuable lessons to provide informal learning fostered by role modeling via online social networks. The Facebook application may be further expanded to enhance female students' interests in IS related careers.

Prediction of Social Influence for Provenance of Misinformation in Online Social Network Using Big Data Approach

2020 ◽  
Author(s):  
Kumaran P ◽  
Rajeswari Sridhar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Law Enforcement ◽  
Online Social Networks ◽  
Large Scale ◽  
Online Social Network ◽  
Root Cause ◽  
Minimum Number ◽  
Targeted Marketing ◽  
Implicit And Explicit

Abstract Online social networks (OSNs) is a platform that plays an essential role in identifying misinformation like false rumors, insults, pranks, hoaxes, spear phishing and computational propaganda in a better way. Detection of misinformation finds its applications in areas such as law enforcement to pinpoint culprits who spread rumors to harm the society, targeted marketing in e-commerce to identify the user who originates dissatisfaction messages about products or services that harm an organizations reputation. The process of identifying and detecting misinformation is very crucial in complex social networks. As misinformation in social network is identified by designing and placing the monitors, computing the minimum number of monitors for detecting misinformation is a very trivial work in the complex social network. The proposed approach determines the top suspected sources of misinformation using a tweet polarity-based ranking system in tandem with sarcasm detection (both implicit and explicit sarcasm) with optimization approaches on large-scale incomplete network. The algorithm subsequently uses this determined feature to place the minimum set of monitors in the network for detecting misinformation. The proposed work focuses on the timely detection of misinformation by limiting the distance between the suspected sources and the monitors. The proposed work also determines the root cause of misinformation (provenance) by using a combination of network-based and content-based approaches. The proposed work is compared with the state-of-art work and has observed that the proposed algorithm produces better results than existing methods.

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.

Working Adults' Buying Intention Through Online Social Network

2021 ◽  
Vol 17 (4) ◽  
pp. 92-116
Author(s):  
Syed Shah Alam ◽  
Chieh-Yu Lin ◽  
Mohd Helmi Ali ◽  
Nor Asiah Omar ◽  
Mohammad Masukujjaman
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Perceived Risk ◽  
Word Of Mouth ◽  
Online Social Network ◽  
Perceived Value ◽  
Online Social Media ◽  
Starting Point

Most businesses have online social media presence; therefore, understanding of working adult's perception on buying through online social networks is vital. The aim of this study is to examine the effect of perceived value, sociability, usability, perceived risk, trust, and e-word-of-mouth on buying intention through online social network sites. The research model for this study was developed based on the literature on information system research. This study adopted convenient sampling of non-probability sampling procedure. Data were collected through self-administered questionnaire, and PLS-based path analysis was used to analyse responses. The findings of the study shows that perceived value, sociability, usability, e-word-of-mouth, attitude, and subjective norm are significant constructs of buying intention through online social networks. This research can serve as a starting point for online shopping research through online social media while encouraging further exploration and integration addition adoption constructs.

scholarly journals The Role of Social Media in Disaster Recovery Following Hurricane Harvey

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Courtney Page-Tan
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Disaster Response ◽  
Disaster Recovery ◽  
Online Social Network ◽  
Stress Test ◽  
High Water ◽  
Houston Area

AbstractHurricane Harvey was social media's first real stress test as a disaster response and recovery mechanism. A confluence of conditions makes it an ideal case study of social media's role in disaster recovery: the lack of a government-issued evacuation order, a call from government leadership for willing and able volunteers with a boat or high-water vehicle to perform life-saving rescues, and wide-spread adoption of social media platforms in the Houston area. While research on online social networks and disasters continues to grow, social scientists know little about how these online networks transform during a crisis and, further, how they drive disaster outcomes. With two original datasets, this study investigates how Houston's online social network transformed during Hurricane Harvey (2017), and the relationship between social media activity and post-Harvey recovery. The findings of a social network analysis (N= 2,387,610) and subsequent statistical analyses reveal the Houston-area online social network grew denser, clustered, and more efficient during the disaster. A spatial analysis and three separate regression models of activity before, during, and after Hurricane Harvey reveal that among 333 Nextdoor Neighborhoods, hyperlocal social media activity was a statistically significant predictor of the rate of rebuilding in these geographically based online communities. These findings suggest that policy and decision-makers should invest into online and offline hyperlocal social networks well before a disaster strikes, and leverage resources and legislation to maintain and strengthen the telecommunications and energy infrastructure that supports access to social media and telecommunications infrastructure during a time of crisis.

Parallel Greedy Algorithm to Multiple Influence Maximization in Social Network

2021 ◽  
Vol 15 (3) ◽  
pp. 1-21
Author(s):  
Guanhao Wu ◽  
Xiaofeng Gao ◽  
Ge Yan ◽  
Guihai Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Parallel Algorithms ◽  
Real World ◽  
Online Social Networks ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Memory Overhead ◽  
Influence Spread ◽  
Real World Applications

Influence Maximization (IM) problem is to select influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget . To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an -approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.

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