scholarly journals Hashkat: large-scale simulations of online social networks

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Kevin Ryczko ◽  
Adam Domurad ◽  
Nicholas Buhagiar ◽  
Isaac Tamblyn
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Scale ◽  
Large Scale Simulations

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.

scholarly journals Online Social Networks (OSN) Evolution Model Based on Homophily and Preferential Attachment

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 654 ◽  
Author(s):  
Jebran Khan ◽  
Sungchang Lee
Keyword(s):  
Social Networks ◽  
Structural Properties ◽  
Online Social Networks ◽  
Large Scale ◽  
Preferential Attachment ◽  
Real Life ◽  
Synthetic Data ◽  
Evolution Model ◽  
Scale Invariant ◽  
Scale Free

In this paper, we propose a new scale-free social networks (SNs) evolution model that is based on homophily combined with preferential attachments. Our model enables the SN researchers to generate SN synthetic data for the evaluation of multi-facet SN models that are dependent on users’ attributes and similarities. Homophily is one of the key factors for interactive relationship formation in SN. The synthetic graph generated by our model is scale-invariant and has symmetric relationships. The model is dynamic and sustainable to changes in input parameters, such as number of nodes and nodes’ attributes, by conserving its structural properties. Simulation and evaluation of models for large-scale SN applications need large datasets. One way to get SN data is to generate synthetic data by using SN evolution models. Various SN evolution models are proposed to approximate the real-life SN graphs in previous research. These models are based on SN structural properties such as preferential attachment. The data generated by these models is suitable to evaluate SN models that are structure dependent but not suitable to evaluate models which depend on the SN users’ attributes and similarities. In our proposed model, users’ attributes and similarities are utilized to synthesize SN graphs. We evaluated the resultant synthetic graph by analyzing its structural properties. In addition, we validated our model by comparing its measures with the publicly available real-life SN datasets and previous SN evolution models. Simulation results show our resultant graph to be a close representation of real-life SN graphs with users’ attributes.

scholarly journals The structure of online social networks modulates the rate of lexical change

2021 ◽  
Author(s):  
Jian Zhu ◽  
David Jurgens
Keyword(s):  
Social Networks ◽  
Online Communities ◽  
Online Social Networks ◽  
Large Scale ◽  
Scale Analysis ◽  
New Words ◽  
Large Scale Analysis ◽  
Lexical Change ◽  
Underlying Network ◽  
The Many

New words are regularly introduced to communities, yet not all of these words persist in a community's lexicon. Among the many factors contributing to lexical change, we focus on the understudied effect of social networks. We conduct a large-scale analysis of over 80k neologisms in 4420 online communities across a decade. Using Poisson regression and survival analysis, our study demonstrates that the community's network structure plays a significant role in lexical change. Apart from overall size, properties including dense connections, the lack of local clusters and more external contacts promote lexical innovation and retention. Unlike offline communities, these topic-based communities do not experience strong lexical levelling despite increased contact but accommodate more niche words. Our work provides support for the sociolinguistic hypothesis that lexical change is partially shaped by the structure of the underlying network but also uncovers findings specific to online communities.

scholarly journals Social Media Use Predicts Greater Liking in In-Person Initial Interactions

2021 ◽  
pp. 014616722110409
Author(s):  
Jennifer L. Heyman ◽  
Lauren Gazzard Kerr ◽  
Lauren J. Human
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Interactions ◽  
Online Social Networks ◽  
Large Scale ◽  
Media Use ◽  
First Impressions ◽  
Social Media Use ◽  
Large Scale Study ◽  
Interaction Partners

Does how people generally engage with their online social networks relate to offline initial social interactions? Using a large-scale study of first impressions ( N = 806, Ndyad = 4,565), we examined how different indicators of social media use relate to the positivity of dyadic in-person first impressions, from the perspective of the participants and their interaction partners. Many forms of social media use (e.g., Instagram, Snapchat, passive) were associated with liking and being liked by others more, although some forms of use (e.g., Facebook, active) were not associated with liking others or being liked by others. Furthermore, most associations held controlling for extraversion and narcissism. Thus, while some social media use may be generally beneficial for offline social interactions, some may be unrelated, highlighting the idea that how, rather than how much, people use social media can play a role in their offline social interactions.

scholarly journals Local structure can identify and quantify influential global spreaders in large scale social networks

2018 ◽  
Vol 115 (29) ◽  
pp. 7468-7472 ◽  
Author(s):  
Yanqing Hu ◽  
Shenggong Ji ◽  
Yuliang Jin ◽  
Ling Feng ◽  
H. Eugene Stanley ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Scale ◽  
Characteristic Number ◽  
Percolation Cluster ◽  
Network Size ◽  
Viral Marketing ◽  
Nucleation Behavior ◽  
Practical Applications ◽  
Entire Network

Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the entire network, any node’s global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long-standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.

scholarly journals Efficient detection of communities with significant overlaps in networks: Partial community merger algorithm

2017 ◽  
Vol 6 (1) ◽  
pp. 71-96 ◽  
Author(s):  
ELVIS H. W. XU ◽  
PAK MING HUI
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Scale ◽  
Partial Information ◽  
Ground Truth ◽  
Ego Networks ◽  
Detection Algorithms ◽  
Efficient Detection ◽  
Merger Process ◽  
Key Issues

AbstractDetecting communities in large-scale social networks is a challenging task where each vertex may belong to multiple communities. Such behavior of vertices and the implied strong overlaps among communities render many detection algorithms invalid. We develop a Partial Community Merger Algorithm (PCMA) for detecting communities with significant overlaps as well as slightly overlapping and disjoint ones. It is a bottom-up approach based on properly reassembling partial information of communities revealed in ego networks of vertices to reconstruct complete communities. We propose a novel similarity measure of communities and an efficient merger process to address the two key issues—noise control and merger order—in implementing this approach. PCMA is tested against two benchmarks and overall it outperforms all compared algorithms in both accuracy and efficiency. It is applied to two huge online social networks, Friendster and Sina Weibo. Millions of communities are detected and they are of higher qualities than the corresponding metadata groups. We find that the latter should not be regarded as the ground-truth of structural communities. The significant overlapping pattern found in the detected communities confirms the need of new algorithms, such as PCMA, to handle multiple memberships of vertices in social networks.

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