AI 中文总结
本研究构建了基于Wikidata和Twitter的数据集，训练多种机器学习模型检测社交网站用户死亡，发现BERT性能最优，死亡后推文消极情感及特定词汇出现频率更高，成功开发高性能自动检测技术。
AI 中文摘要
本论文分析并讨论了死亡前与死亡后社交媒体内容的语言特征差异，并报告了能从与社交网站用户个人资料关联的帖子中自动检测该用户死亡的高性能机器学习（ML）分类器。研究人员使用Wikidata和Twitter构建了新数据集，对传统机器学习模型（RF、KNN、LR、SVM）和深度学习模型（BiLSTM、CNN及当前最先进的BERT）进行训练，这些模型基于TF-IDF提取的特征及预训练词嵌入（Glove、Word2Vec、FastText）对死亡后内容与死亡前内容进行分类。结果显示，RF的性能优于所有其他传统机器学习模型；BiLSTM的性能优于CNN；对于传统模型，TF-IDF的表现始终优于预训练词嵌入；对于深度学习模型，Word2Vec的表现始终优于Glove和FastText；BERT的性能优于所有其他模型。研究还发现，尽管死亡前与死亡后推文表达的积极情感程度相似，但死亡后推文表现出更高的消极情感，而死亡前推文表现出更高的中性情感；消极情感（悲伤、愤怒、惊讶、恐惧）在死亡后推文中更占主导，而快乐在死亡前推文中更占主导。此外，死亡后推文中的词汇、人称代词、动词、家庭相关词汇、宗教词汇、死亡相关词汇及脏话出现频率更高，而死亡前推文中的非人称代词及非正式词汇出现频率更高；死亡后对话比死亡前对话更常体现分析性思维。本实验的重要贡献是成功开发了一种用于自动检测社交网站用户死亡的高性能技术。
英文摘要
This dissertation analysed and discussed the differences in linguistic characteristics between pre-mortem and post-mortem social media content, and reported machine learning (ML) classifiers that achieved high performance in automatically detecting deaths of social networking site users from posts associated with their profiles. A new dataset was developed using Wikidata and Twitter. ML models, both traditional (RF, KNN, LR, and SVM) and deep learning (BiLSTM, CNN, and the state-of-the-art BERT), were trained on features extracted using TF-IDF and pre-trained embeddings (Glove, Word2Vec, and FastText) to classify post-mortem content from its pre-mortem counterpart. The results showed that RF outperformed all other traditional ML models; BiLSTM outperformed CNN; TF-IDF consistently outperformed pre-trained word embeddings for the traditional models; Word2Vec consistently outperformed Glove and FastText for the deep learning models; and BERT outperformed all other models. It was found that although pre-mortem and post-mortem tweets express similar levels of positive sentiment, post-mortem tweets exhibit higher negative sentiment, whereas pre-mortem tweets exhibit higher neutral sentiment. Feelings suggesting negativity (sad, angry, surprise, and fear) are more dominant in post-mortem tweets, while happy is more dominant in pre-mortem tweets. It was also found that words, personal pronouns, verbs, family words, religious words, death words, and swear words occur more frequently in post-mortem tweets, whereas impersonal pronouns and informal words occur more frequently in pre-mortem tweets. Additionally, analytical thinking is expressed more in post-mortem than pre-mortem conversations. This experiment's significant contribution is the successful development of an exceptionally high-performing technique for automatically detecting user deaths on social networking sites.
