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Hate speech and misinformation, spread over social networking services (SNS)such as Facebook and Twitter, have inflamed ethnic and political violence incountries across the globe. We argue that there is limited research on thisproblem within the context of the Global South and present an approach fortackling them. Prior works have shown how machine learning models built withuser-level interaction features can effectively identify users who spreadinflammatory content. While this technique is beneficial in low-resourcelanguage settings where linguistic resources such as ground truth data andprocessing capabilities are lacking, it is still unclear how these interactionfeatures contribute to model performance. In this work, we investigate and showsignificant differences in interaction features between users who spreadinflammatory content and others who do not, applying explainability tools tounderstand our trained model. We find that features with higher interactionsignificance (such as account age and activity count) show higher explanatorypower than features with lower interaction significance (such as name lengthand if the user has a location on their bio). Our work extends researchdirections that aim to understand the nature of inflammatory content inlow-resource, high-risk contexts as the growth of social media use in theGlobal South outstrips moderation efforts.

Why So Inflammatory? Explainability in Automatic Detection of Inflammatory Social Media Users