# Automatic Detection of Deaths from Social Networking Sites

arXiv: https://arxiv.org/abs/2608.05183 · HTML: https://mlsanity.com/paper/2608.05183/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 153 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Fake News Detection on Social Media using Geometric Deep Learning](https://mlsanity.com/paper/1902.06673/) (2019)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [Comparing BERT against traditional machine learning text classification](https://mlsanity.com/paper/2005.13012/) (2020)
- [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1058/) (2014)
- ["Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations](https://mlsanity.com/paper/1904.12991/) (2019)
- [TF-IDF vs Word Embeddings for Morbidity Identification in Clinical Notes: An Initial Study](https://mlsanity.com/paper/2105.09632/) (2021)

## Abstract (arXiv)

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.