# Opinion Dynamics Theory for Analysis of Consensus Formation and Division of Opinion on the Internet

arXiv: https://arxiv.org/abs/1812.11845 · HTML: https://mlsanity.com/paper/1812.11845/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2018.

## Built on by (most cited first, 7 of 7)

- [Perspective in Opinion Dynamics on Complex Convex Domains of Time Networks for Addiction, Forgetting](https://mlsanity.com/paper/2311.15318/) (2023)
- [The Anatomy Spread of Online Opinion Polarization: The Pivotal Role of Super-Spreaders in Social Networks](https://mlsanity.com/paper/2401.01349/) (2023)
- [Note: Evolutionary Game Theory Focus Informational Health: The Cocktail Party Effect Through Werewolfgame under Incomplete Information and ESS Search Method Using Expected Gains of Repeated Dilemmas](https://mlsanity.com/paper/2402.18598/) (2024)
- [Entanglement: Balancing Punishment and Compensation, Repeated Dilemma Game-Theoretic Analysis of Maximum Compensation Problem for Bypass and Least Cost Paths in Fact-Checking, Case of Fake News with Weak Wallace's Law](https://mlsanity.com/paper/2403.02342/) (2024)
- [Note: Harnessing Tellurium Nanoparticles in the Digital Realm Plasmon Resonance, in the Context of Brewster's Angle and the Drude Model for Fake News Adsorption in Incomplete Information Games](https://mlsanity.com/paper/2403.03239/) (2024)
- [Plasmon Resonance Model: Investigation of Analysis of Fake News Diffusion Model with Third Mover Intervention Using Soliton Solution in Non-Complete Information Game under Repeated Dilemma Condition](https://mlsanity.com/paper/2403.05585/) (2024)
- [Introducing First-Principles Calculations: New Approach to Group Dynamics and Bridging Social Phenomena in TeNP-Chain Based Social Dynamics Simulations](https://mlsanity.com/paper/2403.05593/) (2024)

## Abstract (arXiv)

The massive amount of text data on the web has facilitated research on the quantitative analysis of public opinion, which could not be visualized earlier. In this paper, we propose a new opinion dynamics theory. This theory that is intended to explain agreement formation and opinion breakup division in opinion exchanges on social media such as Twitter. With the popularization of the public network, we have become able to communicate with instantaneity and interactivity beyond the temporal and spatial constraints.Research on quantitatively analyzing the distribution of opinion on public opinion that has not been visualized so far utilizing massive web text data is progressing.Our model is based on the Bounded Confidence Model, that expresses opinions in as continuous quantity values. However, in the Bounded Confidence Model, it was assumed that people with different opinions move not in disregard but ignoring opinions. Furthermore, in our theory, it modeled so that it can expresser model incorporates the influence from of the external pressure outside and the phenomenon depending on the surrounding situation.