# Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

arXiv: https://arxiv.org/abs/2402.05571 · HTML: https://mlsanity.com/paper/2402.05571/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 70 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Pretrained Transformers for Text Ranking: BERT and Beyond](https://mlsanity.com/paper/2010.06467/) (2020)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset](https://mlsanity.com/paper/2010.11395/) (2020)
- [RobBERT: a Dutch RoBERTa-based Language Model](https://mlsanity.com/paper/2001.06286/) (2020)
- [Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency](https://mlsanity.com/paper/2006.16736/) (2020)
- [Advancing RNN Transducer Technology for Speech Recognition](https://mlsanity.com/paper/2103.09935/) (2021)
- [Identifying Depression on Twitter](https://mlsanity.com/paper/1607.07384/) (2016)

## Built on by (most cited first, 2 of 2)

- [Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing](https://mlsanity.com/paper/2403.03581/) (2024)
- [Classification of Psychiatry Clinical Notes by Diagnosis: A Deep Learning and Machine Learning Approach](https://mlsanity.com/paper/2508.00695/) (2025)

## Abstract (arXiv)

Background: Eating disorders are increasingly prevalent, and social networks offer valuable information. Objective: Our goal was to identify efficient machine learning models for categorizing tweets related to eating disorders. Methods: Over three months, we collected tweets about eating disorders. A 2,000-tweet subset was labeled for: (1) being written by individuals with eating disorders, (2) promoting eating disorders, (3) informativeness, and (4) scientific content. Both traditional machine learning and deep learning models were employed for classification, assessing accuracy, F1 score, and computational time. Results: From 1,058,957 collected tweets, transformer-based bidirectional encoder representations achieved the highest F1 scores (71.1%-86.4%) across all four categories. Conclusions: Transformer-based models outperform traditional techniques in classifying eating disorder-related tweets, though they require more computational resources.