Personal attacks in the context of social media conversations often lead to fast-paced derailment, leading to even more harmful exchanges being made. State-of-the-art systems for the detection of such conversational derailment often make use of deep learning approaches for prediction purposes. In this paper, we show that an Attention-based BERT architecture, pre-trained on a large Twitter corpus and fine-tuned on our task, is efficient and effective in making such predictions. This model shows clear advantages in performance to the existing LSTM model we use as a baseline. Additionally, we show that this impressive performance can be attained through fine-tuning on a relatively small, novel dataset, particularly after mitigating overfitting issues through synthetic oversampling techniques. By introducing the first transformer based model for forecasting conversational events on Twitter, this work lays the foundation for a practical tool to encourage better interactions on one of the most ubiquitous social media platforms.

使用在大规模的Twitter语料库上预训练并在我们任务上进行微调的基于注意力机制的BERT架构，有效地进行对社交媒体对话中的个人攻击和逻辑偏离行为的预测，此模型相较于我们作为基准所使用的LSTM模型表现出明显的优势。此外，通过综合过采样技术，可在相对较小的新颖数据集上进行微调，并减轻过度拟合问题。通过引入基于Transformer的模型，使该研究为预测Twitter上对话事件的实用工具奠定基础，以鼓励更好地互动在最普遍的社交媒体平台之一。

Twitter上预测不健康对话的哈希化方法