
Community models for malicious content detection on social media graphs often perform well on benchmark datasets but struggle to generalize to new graphs, domains, and tasks. A novel few-shot subgraph sampling approach is proposed to better assess inductive generalization capabilities of these models.


coremsg

evaluating-the-generalization-capabilities-of-community-models-for-malicious-content-detection-on-social-media


Evaluating the Generalization Capabilities of Community Models for Malicious Content Detection on Social Media


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Hate speech detection models evaluated on biased datasets largely overestimate real-world performance on representative Nigerian Twitter data. Domain-adaptive pretraining and finetuning on diverse data are key to maximizing hate speech detection in this low-resource context.


evaluating-hate-speech-detection-on-nigerian-twitter-using-representative-data


Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data
