16 research outputs found
Modelling opinion dynamics under the impact of influencer and media strategies
Digital communication has made the public discourse considerably more complex, and new actors and strategies have emerged as a result of this seismic shift. Aside from the often-studied interactions among individuals during opinion formation, which have been facilitated on a large scale by social media platforms, the changing role of traditional media and the emerging role of “influencers” are not well understood, and the implications of their engagement strategies arising from the incentive structure of the attention economy even less so. Here we propose a novel framework for opinion dynamics that can accommodate various versions of opinion dynamics as well as account for different roles, namely that of individuals, media and influencers, who change their own opinion positions on different time scales. Numerical simulations of instances of this framework show the importance of their relative influence in creating qualitatively different opinion formation dynamics: with influencers, fragmented but short-lived clusters emerge, which are then counteracted by more stable media positions. The framework allows for mean-field approximations by partial differential equations, which reproduce those dynamics and allow for efficient large-scale simulations when the number of individuals is large. Based on the mean-field approximations, we can study how strategies of influencers to gain more followers can influence the overall opinion distribution. We show that moving towards extreme positions can be a beneficial strategy for influencers to gain followers. Finally, our framework allows us to demonstrate that optimal control strategies allow other influencers or media to counteract such attempts and prevent further fragmentation of the opinion landscape. Our modelling framework contributes to a more flexible modelling approach in opinion dynamics and a better understanding of the different roles and strategies in the increasingly complex information ecosystem
Modeling echo chambers and polarization dynamics in social networks
Echo chambers and opinion polarization recently quantified in several
sociopolitical contexts and across different social media, raise concerns on
their potential impact on the spread of misinformation and on openness of
debates. Despite increasing efforts, the dynamics leading to the emergence of
these phenomena stay unclear. We propose a model that introduces the dynamics
of radicalization, as a reinforcing mechanism driving the evolution to extreme
opinions from moderate initial conditions. Inspired by empirical findings on
social interaction dynamics, we consider agents characterized by heterogeneous
activities and homophily. We show that the transition between a global
consensus and emerging radicalized states is mostly governed by social
influence and by the controversialness of the topic discussed. Compared with
empirical data of polarized debates on Twitter, the model qualitatively
reproduces the observed relation between users' engagement and opinions, as
well as opinion segregation in the interaction network. Our findings shed light
on the mechanisms that may lie at the core of the emergence of echo chambers
and polarization in social media
Emergence of polarized ideological opinions in multidimensional topic spaces
Opinion polarization is on the rise, causing concerns for the openness of
public debates. Additionally, extreme opinions on different topics often show
significant correlations. The dynamics leading to these polarized ideological
opinions pose a challenge: How can such correlations emerge, without assuming
them a priori in the individual preferences or in a preexisting social
structure? Here we propose a simple model that qualitatively reproduces
ideological opinion states found in survey data, even between rather unrelated,
but sufficiently controversial, topics. Inspired by skew coordinate systems
recently proposed in natural language processing models, we solidify these
intuitions in a formalism of opinions unfolding in a multidimensional space
where topics form a non-orthogonal basis. Opinions evolve according to the
social interactions among the agents, which are ruled by homophily: two agents
sharing similar opinions are more likely to interact. The model features phase
transitions between a global consensus, opinion polarization, and ideological
states. Interestingly, the ideological phase emerges by relaxing the assumption
of an orthogonal basis of the topic space, i.e. if topics thematically overlap.
Furthermore, we analytically and numerically show that these transitions are
driven by the controversialness of the topics discussed, the more controversial
the topics, the more likely are opinion to be correlated. Our findings shed
light upon the mechanisms driving the emergence of ideology in the formation of
opinions.Comment: 30 pages, 21 figure
Psychological Factors Shaping Public Responses to COVID-19 Digital Contact Tracing Technologies in Germany
The COVID-19 pandemic has seen one of the first large-scale uses of digital contact tracing to track a chain of infection and contain the spread of a virus. The new technology has posed challenges both for governments aiming at high and effective uptake and for citizens weighing its benefits (e.g., protecting others’ health) against the potential risks (e.g., loss of data privacy). Our cross-sectional survey with repeated measures across four samples in Germany ([Formula: see text] ) focused on psychological factors contributing to the public adoption of digital contact tracing. We found that public acceptance of privacy-encroaching measures (e.g., granting the government emergency access to people’s medical records or location tracking data) decreased over the course of the pandemic. Intentions to use contact tracing apps—hypothetical ones or the Corona-Warn-App launched in Germany in June 2020—were high. Users and non-users of the Corona-Warn-App differed in their assessment of its risks and benefits, in their knowledge of the underlying technology, and in their reasons to download or not to download the app. Trust in the app’s perceived security and belief in its effectiveness emerged as psychological factors playing a key role in its adoption. We incorporate our findings into a behavioral framework for digital contact tracing and provide policy recommendations
Technology and Democracy: Understanding the influence of online technologies on political behaviour and decision-making
Drawing from many disciplines, the report adopts a behavioural psychology perspective to argue that “social media changes people’s political behaviour”. Four pressure points are identified and analysed in detail: the attention economy; choice architectures; algorithmic content curation; and mis/disinformation. Policy implications are outlined in detail.JRC.H.1-Knowledge for Policy: Concepts and Method
Perspectives on adaptive dynamical systems
Adaptivity is a dynamical feature that is omnipresent in nature,
socio-economics, and technology. For example, adaptive couplings appear in
various real-world systems like the power grid, social, and neural networks,
and they form the backbone of closed-loop control strategies and machine
learning algorithms. In this article, we provide an interdisciplinary
perspective on adaptive systems. We reflect on the notion and terminology of
adaptivity in different disciplines and discuss which role adaptivity plays for
various fields. We highlight common open challenges, and give perspectives on
future research directions, looking to inspire interdisciplinary approaches.Comment: 46 pages, 9 figure
Perspectives on adaptive dynamical systems
Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches