# Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media

arXiv: https://arxiv.org/abs/1802.09130 · HTML: https://mlsanity.com/paper/1802.09130/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 22 of 22)

- [Claim Detection in Biomedical Twitter Posts](https://mlsanity.com/paper/2104.11639/) (2021)
- [Semi-Supervised Text Classification via Self-Pretraining](https://mlsanity.com/paper/2109.15300/) (2021)
- [Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model](https://mlsanity.com/paper/2204.04521/) (2022)
- [CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://mlsanity.com/paper/2309.05494/) (2023)
- [Semantic Similarity Models for Depression Severity Estimation](https://mlsanity.com/paper/2211.07624/) (2022)
- [Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)](https://mlsanity.com/paper/2204.09952/) (2022)
- [Multi-View Active Learning for Short Text Classification in User-Generated Data](https://mlsanity.com/paper/2112.02611/) (2021)
- [Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers](https://mlsanity.com/paper/2201.11870/) (2022)
- [View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data](https://mlsanity.com/paper/2105.11354/) (2021)
- [A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets](https://mlsanity.com/paper/2204.06337/) (2022)
- [DECK: Behavioral Tests to Improve Interpretability and Generalizability of BERT Models Detecting Depression from Text](https://mlsanity.com/paper/2209.05286/) (2022)
- [Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://mlsanity.com/paper/2403.16614/) (2024)
- [Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training](https://mlsanity.com/paper/2203.01895/) (2022)
- [A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content](https://mlsanity.com/paper/2310.19057/) (2023)
- [Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning](https://mlsanity.com/paper/2405.01597/) (2024)
- [Incorporating Emotions into Health Mention Classification Task on Social Media](https://mlsanity.com/paper/2212.05039/) (2022)
- [Multi-task Learning for Personal Health Mention Detection on Social Media](https://mlsanity.com/paper/2212.05147/) (2022)
- [On the performance of phonetic algorithms in microtext normalization](https://mlsanity.com/paper/2402.02591/) (2024)
- ["Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Posts](https://mlsanity.com/paper/2502.16839/) (2025)
- [Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuning](https://mlsanity.com/paper/2504.21685/) (2025)
- [Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset](https://mlsanity.com/paper/2508.19467/) (2025)
- [Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit](https://mlsanity.com/paper/2602.15504/) (2026)

## Abstract (arXiv)

Millions of users share their experiences on social media sites, such as Twitter, which in turn generate valuable data for public health monitoring, digital epidemiology, and other analyses of population health at global scale. The first, critical, task for these applications is classifying whether a personal health event was mentioned, which we call the (PHM) problem. This task is challenging for many reasons, including typically short length of social media posts, inventive spelling and lexicons, and figurative language, including hyperbole using diseases like "heart attack" or "cancer" for emphasis, and not as a health self-report. This problem is even more challenging for rarely reported, or frequent but ambiguously expressed conditions, such as "stroke". To address this problem, we propose a general, robust method for detecting PHMs in social media, which we call WESPAD, that combines lexical, syntactic, word embedding-based, and context-based features. WESPAD is able to generalize from few examples by automatically distorting the word embedding space to most effectively detect the true health mentions. Unlike previously proposed state-of-the-art supervised and deep-learning techniques, WESPAD requires relatively little training data, which makes it possible to adapt, with minimal effort, to each new disease and condition. We evaluate WESPAD on both an established publicly available Flu detection benchmark, and on a new dataset that we have constructed with mentions of multiple health conditions. Our experiments show that WESPAD outperforms the baselines and state-of-the-art methods, especially in cases when the number and proportion of true health mentions in the training data is small.