Automatic Detection of Deaths from Social Networking Sites

Abstract: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.

Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI)

arXiv:2608.05183 [cs.SI]

(or arXiv:2608.05183v1 [cs.SI] for this version)

https://doi.org/10.48550/arXiv.2608.05183 Focus to learn more arXiv-issued DOI via DataCite

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