Forecasting COVID-19 caseloads using unsupervised embedding clusters of social media posts
We present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states’ COVID-19 subreddits. We benchmark these c...
| প্রধান লেখক: | , , |
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| বিন্যাস: | Conference item |
| ভাষা: | English |
| প্রকাশিত: |
Association for Computational Linguistics
2022
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| _version_ | 1826311954124242944 |
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| author | Drinkall, F Zohren, S Pierrehumbert, JB |
| author_facet | Drinkall, F Zohren, S Pierrehumbert, JB |
| author_sort | Drinkall, F |
| collection | OXFORD |
| description | We present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states’ COVID-19 subreddits. We benchmark these clustered embedding features against features extracted from other high-quality datasets. In a threshold-classification task, we show that they outperform all other feature types at predicting upward trend signals, a significant result for infectious disease modelling in areas where epidemiological data is unreliable. Subsequently, in a time-series forecasting task, we fully utilise the predictive power of the caseload and compare the relative strengths of using different supplementary datasets as covariate feature sets in a transformer-based time-series model. |
| first_indexed | 2024-03-07T08:18:54Z |
| format | Conference item |
| id | oxford-uuid:8f62455e-9cec-4039-bcb4-d554f8f354c4 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:18:54Z |
| publishDate | 2022 |
| publisher | Association for Computational Linguistics |
| record_format | dspace |
| spelling | oxford-uuid:8f62455e-9cec-4039-bcb4-d554f8f354c42024-01-16T12:26:05ZForecasting COVID-19 caseloads using unsupervised embedding clusters of social media postsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:8f62455e-9cec-4039-bcb4-d554f8f354c4EnglishSymplectic ElementsAssociation for Computational Linguistics2022Drinkall, FZohren, SPierrehumbert, JBWe present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states’ COVID-19 subreddits. We benchmark these clustered embedding features against features extracted from other high-quality datasets. In a threshold-classification task, we show that they outperform all other feature types at predicting upward trend signals, a significant result for infectious disease modelling in areas where epidemiological data is unreliable. Subsequently, in a time-series forecasting task, we fully utilise the predictive power of the caseload and compare the relative strengths of using different supplementary datasets as covariate feature sets in a transformer-based time-series model. |
| spellingShingle | Drinkall, F Zohren, S Pierrehumbert, JB Forecasting COVID-19 caseloads using unsupervised embedding clusters of social media posts |
| title | Forecasting COVID-19 caseloads using unsupervised embedding clusters of social media posts |
| title_full | Forecasting COVID-19 caseloads using unsupervised embedding clusters of social media posts |
| title_fullStr | Forecasting COVID-19 caseloads using unsupervised embedding clusters of social media posts |
| title_full_unstemmed | Forecasting COVID-19 caseloads using unsupervised embedding clusters of social media posts |
| title_short | Forecasting COVID-19 caseloads using unsupervised embedding clusters of social media posts |
| title_sort | forecasting covid 19 caseloads using unsupervised embedding clusters of social media posts |
| work_keys_str_mv | AT drinkallf forecastingcovid19caseloadsusingunsupervisedembeddingclustersofsocialmediaposts AT zohrens forecastingcovid19caseloadsusingunsupervisedembeddingclustersofsocialmediaposts AT pierrehumbertjb forecastingcovid19caseloadsusingunsupervisedembeddingclustersofsocialmediaposts |