BERT-based ensemble approaches for hate speech detection - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

BERT-based ensemble approaches for hate speech detection

Résumé

With the freedom of communication provided in online social media, hate speech has increasingly generated. This leads to cyber conflicts affecting social life at the individual and national levels. As a result, hateful content classification is becoming increasingly demanded for filtering hate content before being sent to the social networks. This paper focuses on classifying hate speech in social media using multiple deep models that are implemented by integrating recent transformer-based language models such as BERT, and neural networks. To improve the classification performances, we evaluated with several ensemble techniques, including soft voting, maximum value, hard voting and stacking. We used three publicly available Twitter datasets (Davidson, HatEval2019, OLID) that are generated to identify offensive languages. We fused all these datasets to generate a single dataset (DHO dataset), which is more balanced across different labels, to perform multi-label classification. Our experiments have been held on Davidson dataset and the DHO corpora. The later gave the best overall results, especially F1 macro score, even it required more resources (time execution and memory). The experiments have shown good results especially the ensemble models, where stacking gave F1 score of 97% on Davidson dataset and aggregating ensembles 77% on the DHO dataset
Fichier principal
Vignette du fichier
2209.06505.pdf (593.98 Ko) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04002300 , version 1 (27-02-2024)

Licence

Identifiants

Citer

Khouloud Mnassri, Praboda Rajapaksha, Reza Farahbakhsh, Noel Crespi. BERT-based ensemble approaches for hate speech detection. 2022 IEEE Global Communications Conference (GLOBECOM ), Dec 2022, Rio de Janeiro, Brazil. pp.4649-4654, ⟨10.1109/GLOBECOM48099.2022.10001325⟩. ⟨hal-04002300⟩
14 Consultations
100 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More