In recent years, many troll accounts have emerged to manipulate social media opinion. Detecting and eradicating trolling is a critical issue for social-networking platforms because businesses, abusers, and nation-state-sponsored troll farms use false and automated accounts. NLP techniques are used to extract data from social networking text, such as Twitter tweets. In many text processing applications, word embedding representation methods, such as BERT, have performed better than prior NLP techniques, offering novel breaks to precisely comprehend and categorize social-networking information for various tasks. This paper implements and compares nine deep learning-based troll tweet detection architectures, with three models for each BERT, ELMo, and GloVe word embedding model. Precision, recall, F1 score, AUC, and classification accuracy are used to evaluate each architecture. From the experimental results, most architectures using BERT models improved troll tweet detection. A customized ELMo-based architecture with a GRU classifier has the highest AUC for detecting troll messages. The proposed architectures can be used by various social-based systems to detect troll messages in the future.
翻译:近年来,出现了许多巨魔账户来操纵社交媒体观点。 检测和根除巨魔账户是社会网络平台的一个关键问题,因为企业、滥用者和国家赞助的巨怪农场使用假账户和自动账户。 NLP 技术用于从社交网络文本中提取数据,如推特推特。在许多文本处理应用程序中,词嵌入代表方法,如BERT,比先前的NLP技术表现得更好,为准确理解和分类各种任务的社会网络信息提供了新的突破。本文实施并比较了9个深层次基于学习的巨怪推特检测结构,每个BERT、ELMO和GloVe字嵌入模型有3个模型。精密、回顾、F1评分、AUC和分类精度用于评估每个架构。在实验结果中,大多数使用BERT模型的架构改进了电动机检测。与GRU分类器的定制的基于ELMO的架构拥有用于探测电动信息的最高AUC。 各种基于社会系统的系统可以使用拟议的架构来探测未来的电动信息。