A psychometric analysis of information propagation in online social networks using latent trait theory

Computing ◽  
2015 ◽  
Vol 98 (6) ◽  
pp. 583-607 ◽  
Author(s):  
K. P. Krishna Kumar ◽  
Agrima Srivastava ◽  
G. Geethakumari
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Latent Trait ◽  
Psychometric Analysis ◽  
Information Propagation ◽  
Latent Trait Theory ◽  
Trait Theory

The General Health Questionnaire: a psychometric analysis using latent trait theory

1989 ◽  
Vol 19 (2) ◽  
pp. 469-485 ◽  
Author(s):  
David Andrich ◽  
Lesley Van Schoubroeck
Keyword(s):  
Western Australia ◽  
General Health ◽  
General Health Questionnaire ◽  
Latent Trait ◽  
Macro Level ◽  
Psychometric Analysis ◽  
Health Questionnaire ◽  
Latent Trait Theory ◽  
Trait Theory ◽  
Level Of Analysis

SynopsisThis study examines the Likert-style successive integer scoring of Goldberg's (1972, 1978) General Health Questionnaire (GHQ) with a psychometric model in which the thresholds between successive categories within each item can be estimated. The model is particularly appropriate because the scoring of the successive categories, which are not named in the same way across items, by successive integers has received substantial discussion in the literature. Results from 1967 teachers in Western Australia who completed the 30-item form of the GHQ show that the items conform reasonably well to the model at a general or macro-level of analysis. In particular, the original ordering of categories is supported. However, as expected, there are systematic differences between distances among threshold within items and systematic differences among thresholds between items. The differences between positively and negatively orientated items confirm a suggestion in the literature that these two classes of items form sufficiently different scales so that they could be treated as separate, though reasonably correlated, scales.

scholarly journals A Bio-Inspired Defensive Rumor Confinement Strategy in Online Social Networks

2021 ◽  
Vol 33 (1) ◽  
pp. 47-70
Author(s):  
Santhoshkumar Srinivasan ◽  
Dhinesh Babu L. D.
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Propagation Model ◽  
Information Propagation ◽  
Control Approach ◽  
Rumor Propagation ◽  
Immunization Strategies ◽  
New Information ◽  
The Individual ◽  
Rumor Control

Online social networks (OSNs) are used to connect people and propagate information around the globe. Along with information propagation, rumors also penetrate across the OSNs in a massive order. Controlling the rumor propagation is utmost important to reduce the damage it causes to society. Educating the individual participants of OSNs is one of the effective ways to control the rumor faster. To educate people in OSNs, this paper proposes a defensive rumor control approach that spreads anti-rumors by the inspiration from the immunization strategies of social insects. In this approach, a new information propagation model is defined to study the defensive nature of true information against rumors. Then, an anti-rumor propagation method with a set of influential spreaders is employed to defend against the rumor. The proposed approach is compared with the existing rumor containment approaches and the results indicate that the proposed approach works well in controlling the rumors.

scholarly journals Competition for Attention in Online Social Networks: Implications for Seeding Strategies

2020 ◽  
Author(s):  
Sarah Gelper ◽  
Ralf van der Lans ◽  
Gerrit van Bruggen
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Theoretic Model ◽  
Information Propagation ◽  
Media Campaigns ◽  
Exchange Networks ◽  
Seeding Strategy ◽  
Game Theoretic ◽  
Game Theoretic Model ◽  
Connectivity Parameter

Many firms try to leverage consumers’ interactions on social platforms as part of their communication strategies. However, information on online social networks only propagates if it receives consumers’ attention. This paper proposes a seeding strategy to maximize information propagation while accounting for competition for attention. The theory of exchange networks serves as the framework for identifying the optimal seeding strategy and recommends seeding people that have many friends, who, in turn, have only a few friends. There is little competition for the attention of those seeds’ friends, and these friends are therefore responsive to the messages they receive. Using a game-theoretic model, we show that it is optimal to seed people with the highest Bonacich centrality. Importantly, in contrast to previous seeding literature that assumed a fixed and nonnegative connectivity parameter of the Bonacich measure, we demonstrate that this connectivity parameter is negative and needs to be estimated. Two independent empirical validations using a total of 34 social media campaigns on two different large online social networks show that the proposed seeding strategy can substantially increase a campaign’s reach. The second study uses the activity network of messages exchanged to confirm that the effects are driven by competition for attention. This paper was accepted by Anandhi Bharadwaj, information systems.

Comprehensive Latent Trait Theory

1988 ◽  
Vol 15 (24) ◽  
pp. 1-24 ◽  
Author(s):  
Fumiko Samejima
Keyword(s):  
Latent Trait ◽  
Latent Trait Theory ◽  
Trait Theory

Modelling multi-topic information propagation in online social networks based on resource competition

2016 ◽  
Vol 43 (3) ◽  
pp. 342-355 ◽  
Author(s):  
Liyuan Sun ◽  
Yadong Zhou ◽  
Xiaohong Guan
Keyword(s):  
Social Networks ◽  
Network Structure ◽  
Online Social Networks ◽  
Resource Competition ◽  
Propagation Model ◽  
Small Scale ◽  
Model Parameters ◽  
Information Propagation ◽  
Practical Applications ◽  
The Impact

Understanding information propagation in online social networks is important in many practical applications and is of great interest to many researchers. The challenge with the existing propagation models lies in the requirement of complete network structure, topic-dependent model parameters and topic isolated spread assumption, etc. In this paper, we study the characteristics of multi-topic information propagation based on the data collected from Sina Weibo, one of the most popular microblogging services in China. We find that the daily total amount of user resources is finite and users’ attention transfers from one topic to another. This shows evidence on the competitions between multiple dynamical topics. According to these empirical observations, we develop a competition-based multi-topic information propagation model without social network structure. This model is built based on general mechanisms of resource competitions, i.e. attracting and distracting users’ attention, and considers the interactions of multiple topics. Simulation results show that the model can effectively produce topics with temporal popularity similar to the real data. The impact of model parameters is also analysed. It is found that topic arrival rate reflects the strength of competitions, and topic fitness is significant in modelling the small scale topic propagation.

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