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Updated: Oct 14, 2025
06:18
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
Published on: October 20, 2022
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Rumour prevention in social networks with layer 2 blockchains.
Subhasis Thakur1, John G Breslin1
1National University of Ireland, Galway, Ireland.
Social Network Analysis and Mining
|November 8, 2021
Summary
This study introduces a decentralized method to combat social bots spreading rumors. By enabling users to form trusted networks, this approach significantly reduces rumor propagation, enhancing social media integrity.
Area of Science:
- Computer Science
- Social Network Analysis
- Cybersecurity
Background:
- Social bots pose significant risks to social, political, and economic stability through rumor dissemination.
- Current anti-bot strategies are centralized, facing user distrust due to potential bias.
- Decentralized solutions are needed for greater user acceptance and trust in social media security.
Purpose of the Study:
- To develop and evaluate a decentralized method for preventing social bots from spreading rumors.
- To enhance user trust and privacy in social networks by decentralizing control.
- To reduce the impact of misinformation and malicious actors in online social environments.
Main Methods:
- Users create a secure, privacy-preserving decentralized social network.
- Content acceptance is based on trusted neighbor relationships within the network.
- A decentralized architecture is implemented to manage user interactions and content flow.
Main Results:
- The proposed decentralized method effectively prevents social bots from influencing users.
- A significant reduction in the number of users sharing rumors was demonstrated.
- The system maintains user privacy and security through decentralized neighbor selection.
Conclusions:
- Decentralized social networks offer a viable alternative to centralized systems for combating social bots.
- User-defined trust networks are effective in mitigating the spread of misinformation.
- This approach enhances the resilience of social media platforms against malicious automated actors.
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