RnSIR: A new model of information spread in online social networks

Rumor spreading is a good sample of spreading in which human beings are the main players in the spreading process. Therefore, in order to have a more realistic model of rumor spreading on online social networks, the influence of psycho-sociological factors particularly those which affect users’ reactions toward rumor/anti-rumor should be considered. To this aim, we present a new model that considers the influence of dissenting opinions on those users who have already believed in rumor/anti-rumor but have not spread the rumor/anti-rumor yet. We hypothesize that influence is a motive for the believers to spread their beliefs in rumor/anti-rumor. We derive the stochastic equations of the new model and evaluate it by using two real datasets of rumor spreading on Twitter. The evaluation results support the new hypothesis and show that the novel model which is relied on the new hypothesis is able to better represent rumor spreading.

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Role-Aware Information Spread in Online Social Networks

Entropy ◽

10.3390/e23111542 ◽

2021 ◽

Vol 23 (11) ◽

pp. 1542

Author(s):

Alon Bartal ◽

Kathleen M. Jagodnik

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Future Research ◽

Hybrid Features ◽

Complex Process ◽

Recommendation Algorithms ◽

Information Spread ◽

Comprehensive Survey ◽

Content Recommendation ◽

Future Research Directions

Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.

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A New Model for Rating Users’ Profiles in Online Social Networks

Computer and Information Science ◽

10.5539/cis.v10n2p39 ◽

2017 ◽

Vol 10 (2) ◽

pp. 39 ◽

Cited By ~ 1

Author(s):

Mohammad Malli ◽

Nadine Said ◽

Ahmad Fadlallah

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Dimensional Space ◽

New Model ◽

Qualitative And Quantitative ◽

Cartesian Space ◽

Low Dimensional ◽

Multiple Domains ◽

And Behavior ◽

Orthogonal Coordinates

Profiling users in Online Social Networks (OSNs) is of great benefit in multiple domains (e.g., marketing, sociology, and forensics). In this paper, we propose a new model for rating user’s profile (i.e., low, medium, high, and advanced) in an OSN community by embedding it into clusters located at predefined range of radius in a low-dimensional Cartesian space. The orthogonal coordinates of the profile are estimated using Principle Component Analysis (PCA) applied on a vector of metrics formulated as a set of attributes of interest (i.e., qualitative and quantitative) mined from the user’s profile to characterize his/her level of participation and behavior in the community. The experimentations are conducted on 3000 simulated profiles of three OSNs (Facebook, Twitter and Instagram) by embedding them in three cartesian spaces of three corresponding communities (Religion, Political and Lifestyle). The results show that we are able to estimate accurately the profile rates by reducing the vector of metrics to a low-dimensional space whittle down to 3-D space.

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