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. 2021 Aug 27;16(8):e0256762.
doi: 10.1371/journal.pone.0256762. eCollection 2021.

Understanding international perceptions of the severity of harmful content online

Affiliations

Understanding international perceptions of the severity of harmful content online

Jialun Aaron Jiang et al. PLoS One. .

Abstract

Online social media platforms constantly struggle with harmful content such as misinformation and violence, but how to effectively moderate and prioritize such content for billions of global users with different backgrounds and values presents a challenge. Through an international survey with 1,696 internet users across 8 different countries across the world, this empirical study examines how international users perceive harmful content online and the similarities and differences in their perceptions. We found that across countries, the perceived severity consistently followed an exponential growth as the harmful content became more severe, but what harmful content were perceived as more or less severe varied significantly. Our results challenge platform content moderation's status quo of using a one-size-fits-all approach to govern international users, and provide guidance on how platforms may wish to prioritize and customize their moderation of harmful content.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: After the initial submission of this manuscript, JAJ had taken a position at Facebook. This does not alter our adherence to PLOS ONE policies on sharing data and materials. This also does not impact the data collection and analysis, decision to publish, or preparation of the manuscript.

Figures

Fig 1
Fig 1. Plot of severity value vs. reverse severity rank order for each country under a free-text numeric measurement.
Note that the same rank position may indicate different harmful content for different countries.
Fig 2
Fig 2. Plot of severity value vs. reverse severity rank order for each country under a Likert-scale measurement.
Fig 3
Fig 3. Plot of explained variance vs. number of principal components in PCA.
Fig 4
Fig 4. Plot of average silhouette score vs. number of clusters in Gaussian Mixture models.
Fig 5
Fig 5. Harmful content’s standard deviation vs. sample-wide ranking.
Fig 6
Fig 6. Harmful content’s Δrank vs. sample-wide ranking.

References

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