论文标题
上下文敏感的单词嵌入方法，用于检测巨魔推文
A Context-Sensitive Word Embedding Approach for The Detection of Troll Tweets
论文作者
论文摘要
在这项研究中，我们旨在通过开发和评估一组模型架构来自动检测巨魔推文，以解决社交媒体上拖钓行为的日益关注。利用深度学习技术和预训练的单词嵌入方法（例如Bert，Elmo和Glove），我们使用分类准确性，F1分数，AUC和精度等指标评估了每种体系结构的性能。我们的结果表明，Bert和Elmo嵌入方法的性能要比手套方法更好，这可能是由于它们提供了上下文化的单词嵌入能力，从而更好地捕捉了在线社交媒体中语言使用的细微差别和微妙之处。此外，我们发现CNN和GRU编码者在F1分数和AUC方面的执行方式相似，这表明它们在从输入文本中提取相关信息方面的有效性。发现最佳的方法是使用GRU分类器的基于ELMO的架构，其AUC分数为0.929。这项研究强调了在巨魔推文检测任务中利用上下文化的单词嵌入和适当的编码方法的重要性，这可以帮助基于社交的系统改善其在平台上识别和解决巨魔行为方面的性能。
In this study, we aimed to address the growing concern of trolling behavior on social media by developing and evaluating a set of model architectures for the automatic detection of troll tweets. Utilizing deep learning techniques and pre-trained word embedding methods such as BERT, ELMo, and GloVe, we evaluated the performance of each architecture using metrics such as classification accuracy, F1 score, AUC, and precision. Our results indicate that BERT and ELMo embedding methods performed better than the GloVe method, likely due to their ability to provide contextualized word embeddings that better capture the nuances and subtleties of language use in online social media. Additionally, we found that CNN and GRU encoders performed similarly in terms of F1 score and AUC, suggesting their effectiveness in extracting relevant information from input text. The best-performing method was found to be an ELMo-based architecture that employed a GRU classifier, with an AUC score of 0.929. This research highlights the importance of utilizing contextualized word embeddings and appropriate encoder methods in the task of troll tweet detection, which can assist social-based systems in improving their performance in identifying and addressing trolling behavior on their platforms.
