scholarly journals Cognitive cascades: How to model (and potentially counter) the spread of fake news

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261811
Author(s):  
Nicholas Rabb ◽  
Lenore Cowen ◽  
Jan P. de Ruiter ◽  
Matthias Scheutz
Keyword(s):  
Public Opinion ◽  
Social Science ◽  
Network Science ◽  
Cognitive Model ◽  
Population Level ◽  
Expressive Power ◽  
Belief Function ◽  
Computational Social Science ◽  
Model Sets ◽  
The Individual

Understanding the spread of false or dangerous beliefs—often called misinformation or disinformation—through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual’s set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade model with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and “alternative facts.”

Communication in the Networked Age

2018 ◽  
pp. 2-10
Author(s):  
Brooke Foucault Welles ◽  
Sandra González-Bailón
Keyword(s):  
Social Science ◽  
Network Science ◽  
Analytical Framework ◽  
Computational Social Science ◽  
Human Communication ◽  
Dynamic Nature ◽  
Computational Tools ◽  
Research Challenges ◽  
Communication Processes ◽  
The Individual

This chapter introduces a theoretical and analytical framework that allows the field of communication to adapt to the research challenges of the digital age. In this framework, we use the term ‘networked communication’ to refer to the ability to leverage digital trace data to advance theoretical and empirical research goals. In particular, new data resources and computational tools allow us to improve our understanding of the causes and consequences of communication, spanning levels of analyses that go from the individual to the group and, from there, to organizations, collectives, and societies. The approach we propose here lies at the confluence of network science and computational social science, and it has many different applications as illustrated by the thirty-two chapters that form the Handbook. At the heart of this approach is the principle that we need to embrace the complex and dynamic nature of communication processes. We can only theorize about thoses processes by bringing new empirical light and methodoloigical sophistication to the old questions about the nature, determinants, and outcomes of human communication in an increasingly interconnected environment.

scholarly journals On using centrality to understand importance of entities in the Panama Papers

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248573
Author(s):  
Mayank Kejriwal
Keyword(s):  
Social Science ◽  
Network Science ◽  
Computational Social Science ◽  
Centrality Measures ◽  
Law Firms ◽  
Tax Havens ◽  
The Past ◽  
Past Half Century ◽  
Past Half ◽  
Insight Into

The Panama Papers comprise one of the most recent influential leaks containing detailed information on intermediary companies (such as law firms), offshore entities and company officers, and serve as a valuable source of insight into the operations of (approximately) 214,000 shell companies incorporated in tax havens around the globe over the past half century. Entities and relations in the papers can be used to construct a network that permits, in principle, a systematic and scientific study at scale using techniques developed in the computational social science and network science communities. In this paper, we propose such a study by attempting to quantify and profile the importance of entities. In particular, our research explores whether intermediaries are significantly more influential than offshore entities, and whether different centrality measures lead to varying, or even incompatible, conclusions. Some findings yield conclusions that resemble Simpson’s paradox. We also explore the role that jurisdictions play in determining entity importance.

scholarly journals A methodological framework to predict the individual and population‐level distributions from tracking data

Ecography ◽  
2021 ◽  
Author(s):  
Philippine Chambault ◽  
Tarek Hattab ◽  
Pascal Mouquet ◽  
Touria Bajjouk ◽  
Claire Jean ◽  
...  
Keyword(s):  
Population Level ◽  
Tracking Data ◽  
Methodological Framework ◽  
The Individual

scholarly journals Phenotypic plasticity of labile traits in the wild

2013 ◽  
Vol 59 (4) ◽  
pp. 485-505 ◽  
Author(s):  
Jon E. Brommer
Keyword(s):  
Life History ◽  
Phenotypic Plasticity ◽  
Statistical Approach ◽  
Scale Up ◽  
Population Level ◽  
Wild Populations ◽  
Quantitative Genetic ◽  
In The Wild ◽  
The Individual ◽  
Quantitative Genetic Parameters

Abstract Individual-based studies allow quantification of phenotypic plasticity in behavioural, life-history and other labile traits. The study of phenotypic plasticity in the wild can shed new light on the ultimate objectives (1) whether plasticity itself can evolve or is constrained by its genetic architecture, and (2) whether plasticity is associated to other traits, including fitness (selection). I describe the main statistical approach for how repeated records of individuals and a description of the environment (E) allow quantification of variation in plasticity across individuals (IxE) and genotypes (GxE) in wild populations. Based on a literature review of life-history and behavioural studies on plasticity in the wild, I discuss the present state of the two objectives listed above. Few studies have quantified GxE of labile traits in wild populations, and it is likely that power to detect statistically significant GxE is lacking. Apart from the issue of whether it is heritable, plasticity tends to correlate with average trait expression (not fully supported by the few genetic estimates available) and may thus be evolutionary constrained in this way. Individual-specific estimates of plasticity tend to be related to other traits of the individual (including fitness), but these analyses may be anti-conservative because they predominantly concern stats-on-stats. Despite the increased interest in plasticity in wild populations, the putative lack of power to detect GxE in such populations hinders achieving general insights. I discuss possible steps to invigorate the field by moving away from simply testing for presence of GxE to analyses that ‘scale up’ to population level processes and by the development of new behavioural theory to identify quantitative genetic parameters which can be estimated.

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