arXiv subjects

Yen-Shao Chen

Publications and source records attributed to Yen-Shao Chen.

4 recordsLinked to original sources

arXiv2026

A Bayesian framework for opinion dynamics models

This work introduces a subjective Bayesian framework for the individual update rules used in opinion dynamics models. An individual's initial opinion is represented by the center of a prior belief about an unknown state, and the updated opinion by the posterior mean. After observing a signal, the individual interprets it through a subjective likelihood, which may incorporate perceived bias and noise, and updates the belief by Bayes' rule. Varying the prior and perceived-signal distributions generates four principal response classes: linear updating, saturation, tail rejection, and signal tracking. A sufficiently separated bimodal prior generates local overreaction, mixture signals generate localized attenuation through source attribution, and perceived signal bias generates directional reversal over a finite region. The framework provides Bayesian microfoundations for established response functions and shows that updates often viewed as irrational can be Bayes-consistent under a receiver's subjective beliefs.

econ.TH↗

arXiv2025

Social Media Information Operations

The battlefield of information warfare has moved to online social networks, where influence campaigns operate at unprecedented speed and scale. As with any strategic domain, success requires understanding the terrain, modeling adversaries, and executing interventions. This tutorial introduces a formal optimization framework for social media information operations (IO), where the objective is to shape opinions through targeted actions. This framework is parameterized by quantities such as network structure, user opinions, and activity levels - all of which must be estimated or inferred from data. We discuss analytic tools that support this process, including centrality measures for identifying influential users, clustering algorithms for detecting community structure, and sentiment analysis for gauging public opinion. These tools either feed directly into the optimization pipeline or help defense analysts interpret the information environment. With the landscape mapped, we highlight threats such as coordinated bot networks, extremist recruitment, and viral misinformation. Countermeasures range from content-level interventions to mathematically optimized influence strategies. Finally, the emergence of generative AI transforms both offense and defense, democratizing persuasive capabilities while enabling scalable defenses. This shift calls for algorithmic innovation, policy reform, and ethical vigilance to protect the integrity of our digital public sphere.

cs.SI↗

arXiv2025

Optimizing Influence Campaigns: Nudging under Bounded Confidence

Influence campaigns in online social networks are often run by organizations, political parties, and nation states to influence large audiences. These campaigns are employed through the use of agents in the network that share persuasive content. Yet, their impact might be minimal if the audiences remain unswayed, often due to the bounded confidence phenomenon, where only a narrow spectrum of viewpoints can influence them. Here we show that to persuade under bounded confidence, an agent must nudge its targets to gradually shift their opinions. Using a control theory approach, we show how to construct an agent's nudging policy under the bounded confidence opinion dynamics model and also how to select targets for multiple agents in an influence campaign on a social network. Simulations on real Twitter networks show that a multi-agent nudging policy can shift the mean opinion, decrease opinion polarization, or even increase it. We find that our nudging based policies outperform other common techniques that do not consider the bounded confidence effect. Finally, we show how to craft prompts for large language models, such as ChatGPT, to generate text-based content for real nudging policies. This illustrates the practical feasibility of our approach, allowing one to go from mathematical nudging policies to real social media content.

cs.SI↗

arXiv2024

Shaping Opinions in Social Networks with Shadow Banning

The proliferation of harmful content and misinformation on social networks necessitates content moderation policies to maintain platform health. One such policy is shadow banning, which limits content visibility. The danger of shadow banning is that it can be misused by social media platforms to manipulate opinions. Here we present an optimization based approach to shadow banning that can shape opinions into a desired distribution and scale to large networks. Simulations on real network topologies show that our shadow banning policies can shift opinions and increase or decrease opinion polarization. We find that if one shadow bans with the aim of shifting opinions in a certain direction, the resulting shadow banning policy can appear neutral. This shows the potential for social media platforms to misuse shadow banning without being detected. Our results demonstrate the power and danger of shadow banning for opinion manipulation in social networks.

cs.SI↗