DOI: 10.5061/dryad.jq2bvq8jb
title: Mitigating opinion polarization in social networks using adversarial attacks

description: The proliferation of social networking services has facilitated the
 formation of echo chambers, where similar opinions are amplified, leading
 to increased opinion polarization. While much research has focused on the
 conditions that lead to opinion polarization, methods to mitigate it
 remain underexplored. This study investigates the potential of using
 adversarial attacks to reduce opinion polarization in social networks. By
 introducing small, strategic perturbations to the weights of network
 links, we conducted numerical simulations to observe the effects on
 opinion dynamics. Our results indicate that these perturbations can
 effectively mitigate opinion polarization, with the strength of the effect
 increasing alongside the perturbation parameter. Moreover, larger networks
 exhibited enhanced effectiveness in polarization mitigation. This research
 presents a novel approach to controlling opinion dynamics and offers
 insights into preventing the detrimental effects of polarization in social
 media environments.
description: The data was generated using numerical simulations of opinion
 dynamics in a social network model implemented in the provided Jupyter
 Notebook (<code>Opinion_polarization.ipynb</code>). The model
 considers a binary issue where agents' opinions are influenced by
 their neighbors. Adversarial attacks were simulated by introducing small
 perturbations to the weights of network links. The effectiveness of these
 perturbations in mitigating opinion polarization was evaluated using
 metrics such as the mean absolute value and standard deviation of
 agents' opinions.
description: # Mitigating Opinion Polarization in Social Networks using Adversarial
 Attacks ### Authors: * Michinori Ninomiya (Shizuoka University) * Genki
 Ichinose (Shizuoka University) * Katsumi Chiyomaru (Kyushu Institute of
 Technology) * Kazuhiro Takemoto (Kyushu Institute of Technology) ### Data
 Description: This dataset contains the following file: 1.
 `Opinion_polarization.ipynb`: Jupyter Notebook containing the code used to
 simulate opinion dynamics and adversarial attacks. 2. `Fig1_neutral.csv`:
 Data showing neutral convergence from Fig. 1 (left). Another set of
 simulations with the same parameters as used in the figure. 3.
 `Fig1_radicalization.csv`: Data showing radicalization from Fig. 1
 (center). Another set of simulations with the same parameters as used in
 the figure. 4. `Fig1_polarization.csv`: Data showing polarization from
 Fig. 1 (right). Another set of simulations with the same parameters as
 used in the figure. 5. `Fig2_neutral_control.csv`: Intervention added to
 the left panel in Fig. 1. 6.
 `Fig2_radicalization_control.csv`: Intervention added to the center panel
 in Fig. 1. 7. `Fig2_polarization_control.csv`: Intervention added to the
 right panel in Fig. 1. 8.
 `Fig5_radicalization_delayed_control.csv`: Intervention added at t=1000 to
 the center panel in Fig. 1. 9.
 `Fig5_polarization_delayed_control.csv`: Intervention added at t=1000 to
 the right panel in Fig. 1. 10. `Fig8_polarization_control.csv`: Data of
 polarization control in Fig. 8. 11. `Fig8_random_control.csv`: Data of
 random control in Fig. 8. 12. `Fig9_original.csv`: Data of original
 (without control) in Fig. 9. 13. `Fig9_polarization_control.csv`: Data of
 polarization control in Fig. 9. 14. `Fig9_random_control.csv`: Data of
 random control in Fig. 9. ### Usage Notes: The dataset is intended for use
 in research on opinion dynamics and social network analysis. Researchers
 can replicate the simulations or use the data to explore further the
 effects of adversarial attacks on opinion polarization. The Jupyter
 Notebook includes detailed comments and explanations to facilitate
 understanding and modification of the code. ### Contact Information: For
 any questions or further information, please contact: * Genki Ichinose
 ([ichinose.genki@shizuoka.ac.jp](mailto:ichinose.genki@shizuoka.ac.jp))