Bayesian Social Influence in the Online Realm

Przemyslaw A. Grabowicz; Francisco Romero-Ferrero; Theo Lins; Fabrício Benevenuto; Krishna P. Gummadi; Gonzalo G. de Polavieja

arXiv · 1512.00770

Bayesian Social Influence in the Online Realm

Przemyslaw A. Grabowicz·Francisco Romero-Ferrero·Theo Lins·Fabrício Benevenuto·Krishna P. Gummadi·Gonzalo G. de Polavieja

View original source ↗Read source document ↗Export metadata ↓

Abstract

Our opinions, which things we like or dislike, depend on the opinions of those around us. Nowadays, we are influenced by the opinions of online strangers, expressed in comments and ratings on online platforms. Here, we perform novel "academic A/B testing" experiments with over 2,500 participants to measure the extent of that influence. In our experiments, the participants watch and evaluate videos on mirror proxies of YouTube and Vimeo. We control the comments and ratings that are shown underneath each of these videos. Our study shows that from 5$\%$ up to 40$\%$ of subjects adopt the majority opinion of strangers expressed in the comments. Using Bayes' theorem, we derive a flexible and interpretable family of models of social influence, in which each individual forms posterior opinions stochastically following a logit model. The variants of our mixture model that maximize Akaike information criterion represent two sub-populations, i.e., non-influenceable and influenceable individuals. The prior opinions of the non-influenceable individuals are strongly correlated with the external opinions and have low standard error, whereas the prior opinions of influenceable individuals have high standard error and become correlated with the external opinions due to social influence. Our findings suggest that opinions are random variables updated via Bayes' rule whose standard deviation is correlated with opinion influenceability. Based on these findings, we discuss how to hinder opinion manipulation and misinformation diffusion in the online realm.

Explore related subjects

physics.soc-ph cs.CY cs.SI physics.data-an

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Przemyslaw A. Grabowicz, Francisco Romero-Ferrero, Theo Lins, Fabrício Benevenuto, Krishna P. Gummadi, Gonzalo G. de Polavieja. 2020-02-26. Bayesian Social Influence in the Online Realm. https://arxiv.org/abs/1512.00770

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Quantifying the Dynamics of Innovation Abandonment Across Scientific, Technological, Commercial, and Pharmacological Domains

Despite the vast literature on the diffusion of innovations that impacts a broad range of disciplines, our understanding of the abandonment of innovations remains limited yet is essential for a deeper understanding of the innovation lifecycle. Here, we analyze four large-scale datasets that capture the temporal and structural patterns of innovation abandonment across scientific, technological, commercial, and pharmacological domains. The paper makes three primary contributions. First, across these diverse domains, we uncover one simple pattern of preferential abandonment, whereby the probability for individuals or organizations to abandon an innovation increases with time and correlates with the number of network neighbors who have abandoned the innovation. Second, we find that the presence of preferential abandonment fundamentally alters the way in which the underlying ecosystem breaks down, inducing a novel structural collapse in networked systems commonly perceived as robust against abandonments. Third, we derive an analytical framework to systematically understand the impact of preferential abandonment on network dynamics, pinpointing specific conditions where it may accelerate, decelerate, or have an identical effect compared to random abandonment, depending on the network topology. Together, these results deepen our quantitative understanding of the abandonment of innovation within networked social systems, with implications for the robustness and functioning of innovation communities. Overall, they demonstrate that the dynamics of innovation abandonment follow simple yet reproducible patterns, suggesting that the uncovered preferential abandonment may be a generic property of the innovation lifecycle.

physics.soc-ph↗

arXiv2026

When higher-order interactions matter: reducibility, parsimony, and microscopic organization

The current debate on higher-order interactions raises a fundamental question: when can systems organized through group interactions be faithfully represented within a graph-based formalism? Recent results show that graph descriptions can reproduce higher-order dynamics exactly or preserve selected macroscopic observables. Yet reducibility does not necessarily imply simplification, as information removed from the interaction structure may reappear as complexity in the effective dynamics, while structural features such as nestedness, heterogeneity, and cross-order correlations may be obscured by the reduction. We argue that formal representability alone is insufficient as a criterion for model choice. Both "simpler" and "equivalent" are question-dependent notions: parsimony must be assessed for the complete structure-dynamics model, and adequacy must be defined relative to the observables, scales, and regimes required by the scientific question. A reduced model may therefore be fully adequate for locating a phase transition or identifying a universality class while being inadequate for reproducing microscopic trajectories, transient dynamics, or structure-function relations. When group structure carries relevant microscopic information, higher-order representations may remain the most direct, interpretable, and parsimonious description, as well as the natural framework for understanding how microscopic organization gives rise to macroscopic behavior and functionality.

physics.soc-ph↗

arXiv2026

Emotions as intrinsic colored noise in biological systems

The idea that emotions in biological systems are analogous to intrinsic colored noise is advanced and justified. A model describing the dynamics of operation of biological networks under the influence of colored noise is suggested. The agents of a biological network can be represented either by biological species, such as humans and animals, or by neurons of the brain, or by the nodes of a neural network. Operational actions, or decisions, in a biological noisy network are based not only on the evaluation of utility of alternatives, but also on the agents emotions. The model is probabilistic, with the choice of alternatives characterized by the related probabilities. At the initial step, the agents make decisions individually and then start exchanging information with each other and imitating the actions of other agents, thus forming a biological network of interacting agents. Numerical simulations are accomplished for a heterogeneous society consisting of three groups of agents, one group possessing long-range memory, the other group, short-range memory, and the third group of super-rational agents acting strictly on the basis of utility, being deprived of emotions. Dynamics of opinions in different groups can be smooth, oscillatory, or chaotic. Altogether, eight types of operation are found, depending on the dynamics of group decisions. Under strong imitation effect, there appears chaotic motion in the evolution of decision choice. It is shown how the Ellsberg paradox can be resolved and how the exchange of information influences the dynamics of this paradox.

physics.soc-ph↗