Papers by Victor Orozco-Olvera
HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter (2025.acl-long)
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Manuel Tonneau, Diyi Liu, Niyati Malhotra, Scott A. Hale, Samuel Fraiberger, Victor Orozco-Olvera, Paul Röttger
| Challenge: | Prior work on automated hate speech detection models has been limited due to systematic biases in evaluation datasets and poor performance across geographies. |
| Approach: | They propose to construct a global hate speech dataset representative of social media settings from tweets posted on September 21, 2022. |
| Outcome: | The proposed dataset covers eight languages and four English-speaking countries and covers eight countries where English is the main language on Twitter. |
NaijaHate: Evaluating Hate Speech Detection on Nigerian Twitter Using Representative Data (2024.acl-long)
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Manuel Tonneau, Pedro Quinta De Castro, Karim Lasri, Ibrahim Farouq, Lakshmi Subramanian, Victor Orozco-Olvera, Samuel Fraiberger
| Challenge: | a recent study shows that hate speech detection systems are often evaluated on non-representative samples, raising concerns about overestimating performance in real-world settings. |
| Approach: | They propose a pretrained hate speech detection model that is annotated on a representative sample of Nigerian tweets and propose heuristics for domain-adaptive pretraining and finetuning. |
| Outcome: | The proposed model overestimates real-world performance by at least twofold compared to a dataset from the United States and Nigeria . the proposed model requires ten thousand Nigerian tweets flagged as hateful daily to moderate 60% of hateful content . |