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.
These days different platforms such as social media provide their clients from different backgrounds and languages the possibility to connect and exchange information.
Next, we introduce a bias alleviation mechanism in hate speech detection task to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model.
We believe that this contribution will help the community to better understand the dynamics behind this phenomenon in Instagram, as one of the major social media.
Social and Information Networks
To address these needs, in this study we introduce a novel transfer learning approach based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers).
However, some comments or feedback on SNSs are inconsiderate and offensive, and sometimes this type of feedback has a very negative effect on a target user.
Nowadays, a big part of people rely on available content in social media in their decisions (e. g. reviews and feedback on a topic or product).