eprintid: 10187977 rev_number: 8 eprint_status: archive userid: 699 dir: disk0/10/18/79/77 datestamp: 2024-02-27 17:57:57 lastmod: 2024-02-27 17:57:57 status_changed: 2024-02-27 17:57:57 type: proceedings_section metadata_visibility: show sword_depositor: 699 creators_name: Aduragba, Olanrewaju Tahir creators_name: Yu, Jialin creators_name: Cristea, Alexandra I title: Incorporating Emotions into Health Mention Classification Task on Social Media ispublished: pub divisions: UCL divisions: B04 divisions: C06 divisions: F61 keywords: health mention classification, emotion detection, social media, natural language processing note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. 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. date: 2024-01-22 date_type: published publisher: IEEE official_url: https://doi.org/10.1109/bigdata59044.2023.10386330 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2252329 doi: 10.1109/bigdata59044.2023.10386330 isbn_13: 979-8-3503-2445-7 lyricists_name: Yu, Jialin lyricists_id: JJYUX24 actors_name: Yu, Jialin actors_id: JJYUX24 actors_role: owner full_text_status: public pres_type: paper series: IEEE International Conference on Big Data (BigData) publication: 2023 IEEE International Conference on Big Data (BigData) volume: 2023 place_of_pub: Sorrento, Italy pagerange: 4834-4842 event_title: 2023 IEEE International Conference on Big Data (BigData) event_dates: 15 Dec 2023 - 18 Dec 2023 book_title: Proceedings of the 2023 IEEE International Conference on Big Data (BigData) citation: 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. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10187977/2/Yu_2212.05039.pdf