Abstract
As user-generated online content has been flourishing with both useful information and misinformation. One of the complexities surrounding such phenomena is its huge amounts of data and its associated difficulties to effectively moderate content, particularly as most initiatives are centralised and fraught with its intrinsic trust issues. One of the few examples using mainly a decentralised (i.e., community-driven) mechanism is Twitter’s Community Notes (once named as Birdwatch) experimental project. This paper thus is about testing the efficiency of such community-based content moderation mechanism and scenarios of interest aiming to better understanding how the users themselves better moderate online content. This is done through an agent-based approach and three conclusions are discussed in detail: (1) to some extent the community is able to fight against misinformation, (2) a Birdwatch-like mechanism can indeed boost the community’s content moderation ability, but there is a nontrivial trade-off between social influence and content timeliness and (3) a simple proposition, in the form of a reminder mechanism to users, cannot fulfil the task of improving the content moderation efficiency, which means a different approach to design is needed.
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Data availability
The code of agent-based model and the data generated from the model can be accessed by emailing the corresponding author: wangchenlong@gacrnd.com or yunming_wang@outlook.com.
Notes
The probability of a user posting a Tweet in each time step is smaller than 1. In this model, it is set to 0.2.
This amount was chosen based on the requirement for running Birdwatch note ranking algorithms and the computing resources the author could afford at the time of writing this paper. Each run requires from 30 to 50 min using one combination of the parameters on a computer with AMD Ryzen 7 5800H CPU and 16 GB RAM memory.
The author uses “decide” not to verification only for descriptive simplicity. Users usually are to inpatient or careless to carry out verification in reality. But the description does not affect the algorithm running in the model.
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Acknowledgements
Both authors thank participants to the internal seminars organised by the Geary Institute in UCD.
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There is no specific funding attached to this research output. The first author (i.e., Chenlong Wang) is self-funded, and he earns salary by taking tutorial classes in UCD and salary earned in GAC R&D. The second author (i.e., Pablo Lucas) is supported by UCD employee salary.
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Wang, C., Lucas, P. Efficiency of Community-Based Content Moderation Mechanisms: A Discussion Focused on Birdwatch. Group Decis Negot (2024). https://doi.org/10.1007/s10726-024-09881-1
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DOI: https://doi.org/10.1007/s10726-024-09881-1