A general framework to link theory and empirics in opinion formation models

Kozitsin, Ivan V.

doi:10.1038/s41598-022-09468-3

Cited by 42 publications

(21 citation statements)

References 71 publications

(73 reference statements)

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“…Following the approach from Ref. (Kozitsin, 2022), we model opinion updates as a function of the focal agent's opinion and the element's properties. Being exposed to the textual element of the news feed, the agent can switch its current opinion 𝑜 𝑖,𝑡 ∈ Ξ to one of 𝑚 alternatives Ξ 1 , … , Ξ 𝑚 with some probabilities that add up to one.…”

Section: Opinion Update Protocolmentioning

confidence: 99%

Opinion Formation in Online Public Debates Structured in Information Cascades: A System-Theoretic Viewpoint

Kozitsin¹

2023

Preprint

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Information cascades (tree-like structures formed by posts, comments, likes, replies, etc.) constitute the spine of the public online information environment, reflecting its various trends, evolving with it and, importantly, affecting its development. While users participate in online discussions, they display their views and thus contribute to the growth of cascades. At the same time, users’ opinions are influenced by cascades’ elements. The current paper aims to advance our knowledge regarding this social phenomenon by developing an agent-based model in which agents participate in a discussion around a post on the Internet. Agents display their opinions by writing comments on the post and liking them (i.e., leaving positive assessments). The result of these processes is dual: on the one hand, agents develop an information cascade; on the other hand, they update their views. Our purpose is to understand how agents’ activity, openness to influence, and cognitive constraints (that condition the amount of information individuals are able to proceed with) affect opinion dynamics. We also control for social contagions (when people’ perception of a message may depend not only on the message’s opinion, but also on how other individuals perceive this object, with more positive evaluations increasing the probability of adoption) and ranking algorithms that steer the order in which agents learn new messages. Among other things, we demonstrated that replies to disagreeable opinions are extremely effective for promoting your own position. In contrast, likes have a tiny effect on this issue.

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Section: Opinion Update Protocolmentioning

confidence: 99%

Opinion Formation in Online Public Debates Structured in Information Cascades: A System-Theoretic Viewpoint

Kozitsin¹

2023

Preprint

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“…This work subscribes to a series of papers dealing with the same goal of building tools to allow comparing the various models of opinion dynamics [ 9 , 10 , 11 ].…”

Section: Opinion Dynamics Models and Realitymentioning

confidence: 99%

Opinion Dynamics and Unifying Principles: A Global Unifying Frame

Galam

2022

Entropy

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I review and extend the set of unifying principles that allow comparing all models of opinion dynamics within one single frame. Within the Global Unifying Frame (GUF), any specific update rule chosen to study opinion dynamics for discrete individual choices is recast into a probabilistic update formula. The associated dynamics is deployed using a general probabilistic sequential process, which is iterated via the repeated reshuffling of agents between successive rounds of local updates. The related driving attractors and tipping points are obtained with non-conservative regimes featuring both threshold and threshold-less dynamics. Most stationary states are symmetry broken, but fifty–fifty coexistence may also occur. A practical procedure is exhibited for several versions of Galam and Sznajd models when restricted to the use of three agents for the local updates. Comparing these various models, some are found to be identical within the GUF. Possible discrepancies with numerical simulations are discussed together with the difference between the GUF procedure and a mean field approach.

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“…This work subscribes to a series of papers dealing with the same goal of building tools to allow comparing the various models of opinion dynamics [9][10][11].…”

Section: Opinion Dynamics Models and Realitymentioning

confidence: 99%

“…and D = 81 > 0, (10) which implies three real roots p c,1 < p c,2 < p c,3 , which are p c,1 = 0, p c,2 = 1/2, p c,3 = 1.…”

Section: Applying the Guf To Existing Opinion Dynamics Modelsmentioning

confidence: 99%

Opinion Dynamics and Unifying Principles: A Global Unifying Frame

Galam

2022

Preprint

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I review and extend the set of unifying principles, which allow comparing all models of opinion dynamics within one single frame. Within the Global Unifying Frame (GUF), any specific update rule chosen to study opinion dynamics for discrete individual choices is recast into a probabilistic update formula. The associated dynamics is deployed using a general probabilistic sequential process, which is iterated via the repeated reshuffling of agents between successive rounds of local updates. The related driving attractors and tipping points are obtained with non-conservative regimes featuring both threshold and threshold-less dynamics. Most stationary states are symmetry broken, but fifty-fifty coexistence may also occur. A practical procedure is exhibited for several versions of Galam and Sznajd models when restricted to the use of three agents for the local updates. Comparing these various models, some are found to be identical within the GUF. Possible discrepancies with numerical simulations are discussed together with the difference between the GUF procedure and a mean field approach.

show abstract

A general framework to link theory and empirics in opinion formation models

Cited by 42 publications

References 71 publications

Opinion Formation in Online Public Debates Structured in Information Cascades: A System-Theoretic Viewpoint

Opinion Formation in Online Public Debates Structured in Information Cascades: A System-Theoretic Viewpoint

Opinion Dynamics and Unifying Principles: A Global Unifying Frame

Opinion Dynamics and Unifying Principles: A Global Unifying Frame

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