Aduragba, OT;
Yu, J;
Cristea, A;
Long, Y;
(2023)
Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance.
In:
WWW '23: Proceedings of the ACM Web Conference 2023.
(pp. pp. 3928-3936).
ACM
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Abstract
People often use disease or symptom terms on social media and online forums in ways other than to describe their health. Thus the NLP health mention classification (HMC) task aims to identify posts where users are discussing health conditions literally, not figuratively. Existing computational research typically only studies health mentions within well-represented groups in developed nations. Developing countries with limited health surveillance abilities fail to benefit from such data to manage public health crises. To advance the HMC research and benefit more diverse populations, we present the Nairaland health mention dataset (NHMD), a new dataset collected from a dedicated web forum for Nigerians. NHMD consists of 7,763 manually labelled posts extracted based on four prevalent diseases (HIV/AIDS, Malaria, Stroke and Tuberculosis) in Nigeria. With NHMD, we conduct extensive experiments using current state-of-the-art models for HMC and identify that, compared to existing public datasets, NHMD contains out-of-distribution examples. Hence, it is well suited for domain adaptation studies. The introduction of the NHMD dataset imposes better diversity coverage of vulnerable populations and generalisation for HMC tasks in a global public health surveillance setting. Additionally, we present a novel multi-task learning approach for HMC tasks by combining literal word meaning prediction as an auxiliary task. Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods statistically significantly (p < 0.01, Wilcoxon test) in terms of F1 score over the state-of-the-art and shows that our new dataset poses a strong challenge to the existing HMC methods.
| Type: | Proceedings paper |
|---|---|
| Title: | Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance |
| Event: | WWW '23 |
| ISBN-13: | 9781450394161 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3543507.3583877 |
| Publisher version: | https://doi.org/10.1145/3543507.3583877 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Datasets, Health Mention Classifcation, Public Health Surveillance, Multi-task Learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10171115 |
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