Aduragba, Olanrewaju Tahir;
Yu, Jialin;
Cristea, Alexandra I;
(2024)
Incorporating Emotions into Health Mention Classification Task on Social Media.
In:
Proceedings of the 2023 IEEE International Conference on Big Data (BigData).
(pp. pp. 4834-4842).
IEEE: Sorrento, Italy.
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Abstract
The Health Mention Classification (HMC) task plays a pivotal role in leveraging social media discourse for public health mention monitoring, particularly in identifying and tracking disease proliferation. Despite its potential, the task poses significant challenges, due to the nuanced nature of health-related discussion. Building upon recent insights that emotional context can enhance HMC performance, in this paper, we explore how affective information can be integrated into the HMC process. Our study pioneers two distinct methodological pipelines that are designed to embed emotional nuances into the HMC task: (1) a two-stage fine-tuning process, starting with an implicit affective knowledge injection to initialise the model, followed by intermediate HMC task fine-tuning; and (2) an explicit multi-feature fusion strategy to leverage affective knowledge for HMC prediction. We conducted comprehensive evaluations across five diverse HMC benchmark datasets, encompassing content from Twitter, Reddit, and a blend of other social media platforms. Our empirical findings reveal that our affective-enriched models achieve statistically significant improvements across HMC benchmarks. Notably, the explicit multi-feature fusion method yielded a minimum of 3% improvement in F1 score over established BERT baselines across all tested corpora. Intriguingly, our analysis also suggests that the exclusive consideration of negative emotional indicators does not detrimentally impact HMC efficacy compared to leveraging both positive and negative emotions. Furthermore, our affectiveness-aware models demonstrate promising utility as a viable alternative in scenarios where domain-specific HMC datasets are scarce or non-existent for fine-tuning purposes. The consistent performance uplift across datasets sourced from varied social media channels underscores the generalisability and resilience of our proposed framework, marking a significant step forward in the computational understanding of health-related conversations in the digital sphere.
| Type: | Proceedings paper |
|---|---|
| Title: | Incorporating Emotions into Health Mention Classification Task on Social Media |
| Event: | 2023 IEEE International Conference on Big Data (BigData) |
| Dates: | 15 Dec 2023 - 18 Dec 2023 |
| ISBN-13: | 979-8-3503-2445-7 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/bigdata59044.2023.10386330 |
| Publisher version: | https://doi.org/10.1109/bigdata59044.2023.10386330 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | health mention classification, emotion detection, social media, natural language processing |
| 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/10187977 |
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