Opinion dynamics on interacting networks: media competition and social influence

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

doi:10.1038/srep04938

Cited by 180 publications

(136 citation statements)

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“…The features of online discussions about these and other topics can be mapped and as discussed in Section 9 used to model behavior-vaccination dynamics [738,739,[744][745][746][747]. Interestingly, different opinions (pro and anti vaccination) induce effects of polarization in which individuals of different ideas do not interact much.…”

Section: Social Media Based Methodsmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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Section: Social Media Based Methodsmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…Researches also demonstrate that emotions embedded in the contents (Berger & Milkman, 2012), types and size of media, interaction patterns of the information system are all contributive factors on the formation, trends and diffusion of the public opinions (Quattrociocchi, Caldarelli, & reports, 2014). How to recognize the behavioural patterns of information exchanging between the situation model layer and social view layer and how public and individual opinions evolves accordingly is still an open question and attracts continuous research interests in this area (Bakshy, Messing, & Science, 2015;Berger & Milkman, 2012;Quattrociocchi et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

An Extended Cognitive Situation Model for Capturing Subjective Dynamics of Events from Social Media

Wang

Alahakoon

Silva

2018

AJIS

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The event-indexing situation models are introduced as event models derived from language to facilitate comprehension and memory retrieval. These models explain how fragmental information about events are collected, integrated and updated into a coherent set of views of what the text is about. The models are adopted as the basis of this study as an attempt to capture the event with contextual, dynamic, and social features, as conveyed by the vast volumes of online textual resources. Information in social media is received through highly personalized channels and is reshaped and interpreted in a more individual, segmental and real-time manner. The reprocessed information is then spread at high speed to a wider range of receivers. Therefore, the interpretation of mainstream media content is influenced by ongoing and dynamic contribution of opinions by users empowered by social media. This new phenomenon has not been examined so far from the perspective of the impact on conventional situation models. This paper explores how collaborative and sharing aspects of social media emphasize subjectivity of interpretation of mainstream media and proposes an extended cognitive situation model which better represents event-centric knowledge. This study investigates the mechanisms for constructing and updating the situation models with continuous textual information streamed from heterogeneous forms of media. It also empirically demonstrates how the proposed model can enhance the understanding of subjective aspects of events with dynamic social opinions.

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“…The dynamics of the formation of collective opinions have been studied in a literature using stylised mathematical simulations models (Sznajd-Weron and Sznajd 2000; Weisbuch et al 2002;Deffuant et al 2000Deffuant et al , 2002Hegselmann and Krause 2002;Castellano et al 2009;Quattrociocchi et al 2014;Moussaïd 2013), for instance, develops an ABM of opinion dynamics addressing how the collective opinion of risk such as attitude towards climate change is formed through exchanges of individual risk judgments. In this model, agents are initially exposed to the same information through mass media, but agents differ in terms of probability to search for new information.…”

Section: Household Migration In Abmsmentioning

confidence: 99%

Household migration in disaster impact analysis: incorporating behavioural responses to risk

Husby

Koks

2017

Nat Hazards

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Detailed estimates of economy-wide disaster losses provide important inputs for disaster risk management. The most common models used to estimate losses are inputoutput (IO) and computable general equilibrium (CGE) models. A key strength of these models is their ability to capture the ripple effects, whereby the impacts of a disaster are transmitted to regions and sectors that are not directly affected by the event. One important transmission channel is household migration. Changes in the spatial distribution of people are likely to have substantial impacts on local labour and housing markets. In this paper, we argue that IO and CGE models suffer from limitations in representing household migration under disaster risk. We suggest combining IO and CGE models with agent-based models to improve the representation of migration in disaster impact analysis.

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Opinion dynamics on interacting networks: media competition and social influence

Cited by 180 publications

References 50 publications

Statistical physics of vaccination

Statistical physics of vaccination

An Extended Cognitive Situation Model for Capturing Subjective Dynamics of Events from Social Media

Household migration in disaster impact analysis: incorporating behavioural responses to risk

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